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SrREBjZwYY | 【Proposal】Pokémon Battle Agent based on LLMs | [
"Zihan Lv",
"Cen Qihang"
] | The rapid development of LLMs has led to widespread applications in interactive environments, particularly in gaming, where LLM agents demonstrate impressive decision-making and strategy execution capabilities. This paper focuses on developing a Pokémon battle agent based on LLMs. We combine techniques such as supervised fine-tuning, KAG and Self-Consistency to improve agent's contextual understanding and generate effective battle commands. Through experiments on Pokémon Showdown with robots and human, we will evaluate the model's win rates and strategic performance, aiming to contribute to the development of LLM agents capable of dynamic and complex environments. | [
"LLMs",
"SFT",
"Knowledge-Augmented Generation",
"Self-Consistency"
] | https://openreview.net/pdf?id=SrREBjZwYY | tYPrUfaN5e | official_review | 1,731,052,186,125 | SrREBjZwYY | [
"everyone"
] | [
"~Zijun_Liu2"
] | title: Review and Feedback
review: ## Overview
The proposal for a Pokémon battle agent leveraging large language models (LLMs) is intriguing, targeting the development of an AI agent that can effectively strategize within Pokémon battles. The authors aim to achieve this by utilizing supervised fine-tuning, knowledge-augmented generation (KAG), and self-consistency to enhance contextual understanding and tactical decision-making. The project’s experiment design is clear to evaluate win rates and strategic capability against both automated systems and human opponents on Pokémon Showdown.
## Strengths
1. **Clear Focus on Techniques**: The proposal systematically addresses key techniques—KAG, Chain-of-Thought (CoT), and self-consistency. Each method has a distinct purpose, showing a thoughtful approach to the technical challenges of developing a decision-making agent.
2. **Relevance to Broader Research Trends**: By positioning this project within the context of games as testing environments for general AI, the proposal aligns with trends in AI research exploring LLMs in strategic, interactive scenarios. This adds value by contributing to foundational research that might inform the development of autonomous decision-making agents in varied applications beyond gaming.
## Suggestions for Improvement
1. **More Reference Needed**: The proposal could benefit from more references on some terms mentioned, e.g., KAG. This will help readers understand the context and relevance of these techniques in the broader AI literature.
2. **Novelty Statement**: The proposal could be strengthened by a more explicit statement on the novelty of the project. Currently, approach 1 shows bare differences from existing methods. Highlighting the unique aspects of the project would make it more compelling. Still, the novelty of the project is clear in approach 2.
rating: 8
confidence: 3 |
SrREBjZwYY | 【Proposal】Pokémon Battle Agent based on LLMs | [
"Zihan Lv",
"Cen Qihang"
] | The rapid development of LLMs has led to widespread applications in interactive environments, particularly in gaming, where LLM agents demonstrate impressive decision-making and strategy execution capabilities. This paper focuses on developing a Pokémon battle agent based on LLMs. We combine techniques such as supervised fine-tuning, KAG and Self-Consistency to improve agent's contextual understanding and generate effective battle commands. Through experiments on Pokémon Showdown with robots and human, we will evaluate the model's win rates and strategic performance, aiming to contribute to the development of LLM agents capable of dynamic and complex environments. | [
"LLMs",
"SFT",
"Knowledge-Augmented Generation",
"Self-Consistency"
] | https://openreview.net/pdf?id=SrREBjZwYY | nNdu1vpMcw | official_review | 1,731,314,300,012 | SrREBjZwYY | [
"everyone"
] | [
"~Jin_Zhu_Xu1"
] | title: Clear idea topic
review: The proposal explains a clear and creative idea topic, but not convincing enough on how the proposed techniques is applicable to the targeted objectives. The proposed techniques explanation is too general and not clear on how specific technical improvements will impact gameplay mechanics, as well as how the evaluation framework works
rating: 7
confidence: 4 |
SrREBjZwYY | 【Proposal】Pokémon Battle Agent based on LLMs | [
"Zihan Lv",
"Cen Qihang"
] | The rapid development of LLMs has led to widespread applications in interactive environments, particularly in gaming, where LLM agents demonstrate impressive decision-making and strategy execution capabilities. This paper focuses on developing a Pokémon battle agent based on LLMs. We combine techniques such as supervised fine-tuning, KAG and Self-Consistency to improve agent's contextual understanding and generate effective battle commands. Through experiments on Pokémon Showdown with robots and human, we will evaluate the model's win rates and strategic performance, aiming to contribute to the development of LLM agents capable of dynamic and complex environments. | [
"LLMs",
"SFT",
"Knowledge-Augmented Generation",
"Self-Consistency"
] | https://openreview.net/pdf?id=SrREBjZwYY | iot2nFGqwK | official_review | 1,731,333,136,344 | SrREBjZwYY | [
"everyone"
] | [
"~Ziyu_Zhao6"
] | title: Review of "Pokémon Battle Agent based on LLMs" Proposal
review: Overview:
This proposal presents a project to develop a contextually aware Pokémon battle agent using Large Language Models (LLMs). The project aims to enhance user engagement by allowing the agent to output Pokémon battle commands dynamically and accurately. The proposed method combines two main approaches: supervised fine-tuning on Pokémon battle datasets and knowledge-augmented generation (KAG) with self-consistency mechanisms.
Strengths:
1. Novel and Engaging Use Case: The proposal leverages a highly popular and relatable franchise (Pokémon) to explore the potential of LLMs in an interactive gaming environment, which could draw significant interest in gaming and education sectors.
2. Integration of Advanced NLP Techniques: By combining knowledge-augmented generation and self-consistency techniques, the proposal addresses a key challenge in generative AI—reducing hallucinations and improving contextual understanding.
Weaknesses:
1. Method Novelty: While KAG and self-consistency are proposed to mitigate hallucinations, the proposal's novel points are not well clearly stated compared to related works.
2. Evaluation Metrics Ambiguity: Although win rates are mentioned, other performance metrics (e.g., decision-making latency and user satisfaction) are not discussed in detail. Adding these would provide a more comprehensive evaluation framework for the proposed system’s success in real-world applications.
rating: 7
confidence: 4 |
SrREBjZwYY | 【Proposal】Pokémon Battle Agent based on LLMs | [
"Zihan Lv",
"Cen Qihang"
] | The rapid development of LLMs has led to widespread applications in interactive environments, particularly in gaming, where LLM agents demonstrate impressive decision-making and strategy execution capabilities. This paper focuses on developing a Pokémon battle agent based on LLMs. We combine techniques such as supervised fine-tuning, KAG and Self-Consistency to improve agent's contextual understanding and generate effective battle commands. Through experiments on Pokémon Showdown with robots and human, we will evaluate the model's win rates and strategic performance, aiming to contribute to the development of LLM agents capable of dynamic and complex environments. | [
"LLMs",
"SFT",
"Knowledge-Augmented Generation",
"Self-Consistency"
] | https://openreview.net/pdf?id=SrREBjZwYY | ZAh1ZGd9RU | official_review | 1,731,415,263,653 | SrREBjZwYY | [
"everyone"
] | [
"~Kittaphot_Saengprachathanarak1"
] | title: Review of "Pokemon Battle Agent based on LLMs"
review: The paper presents a Pokémon battle agent based on large language models (LLMs) that aims to improve agent performance through supervised fine-tuning, knowledge-augmented generation (KAG), and self-consistency. By incorporating domain-specific knowledge and enhancing the model’s reasoning, it attempts to build a contextually aware agent capable of handling dynamic battle scenarios. The experiments will be conducted using the Pokémon Showdown platform, where the agent's strategic decision-making will be evaluated against both robots and humans. While the proposed methods, such as integrating external knowledge and fine-tuning with battle replays, are innovative, further validation and comparison with existing Pokémon battle agents would strengthen the contribution. This work is an interesting step forward in using LLMs for dynamic, game-based decision-making.
rating: 8
confidence: 4 |
SrREBjZwYY | 【Proposal】Pokémon Battle Agent based on LLMs | [
"Zihan Lv",
"Cen Qihang"
] | The rapid development of LLMs has led to widespread applications in interactive environments, particularly in gaming, where LLM agents demonstrate impressive decision-making and strategy execution capabilities. This paper focuses on developing a Pokémon battle agent based on LLMs. We combine techniques such as supervised fine-tuning, KAG and Self-Consistency to improve agent's contextual understanding and generate effective battle commands. Through experiments on Pokémon Showdown with robots and human, we will evaluate the model's win rates and strategic performance, aiming to contribute to the development of LLM agents capable of dynamic and complex environments. | [
"LLMs",
"SFT",
"Knowledge-Augmented Generation",
"Self-Consistency"
] | https://openreview.net/pdf?id=SrREBjZwYY | TBgJ5gWVTC | official_review | 1,731,168,721,951 | SrREBjZwYY | [
"everyone"
] | [
"~Bryan_Constantine_Sadihin1"
] | title: Review of "Pokemon Battle Agent based on LLMs"
review: Strengths:
1. Innovative Application: The topic of using LLM within Pokemon battle context is a creative approach, which showcases the potential of LLMs in interactive gaming environments.
2. Specific defined Techniques: The proposal has defined some probable method that is going to be used, such as KAG and Self-Consistency, alongside fine-tuning with battle replays data.
Cons:
1. Inexistence of Metric: The paper could have been improved by mentioning specific optimization metric for training, validation, and testing
2. Limited Generalizability: As the focus of this paper is solely for Pokemon battles, it limits the direct applicability beyond this domain. Applying to other domain can't directly implementing the same research without major modification.
rating: 8
confidence: 4 |
SrREBjZwYY | 【Proposal】Pokémon Battle Agent based on LLMs | [
"Zihan Lv",
"Cen Qihang"
] | The rapid development of LLMs has led to widespread applications in interactive environments, particularly in gaming, where LLM agents demonstrate impressive decision-making and strategy execution capabilities. This paper focuses on developing a Pokémon battle agent based on LLMs. We combine techniques such as supervised fine-tuning, KAG and Self-Consistency to improve agent's contextual understanding and generate effective battle commands. Through experiments on Pokémon Showdown with robots and human, we will evaluate the model's win rates and strategic performance, aiming to contribute to the development of LLM agents capable of dynamic and complex environments. | [
"LLMs",
"SFT",
"Knowledge-Augmented Generation",
"Self-Consistency"
] | https://openreview.net/pdf?id=SrREBjZwYY | QmGsqlTwc3 | official_review | 1,731,162,371,817 | SrREBjZwYY | [
"everyone"
] | [
"~Lei_Wu17"
] | title: Evaluation of "Pokémon Battle Agent based on LLMs" Proposal
review: # Pros
* Well-Defined Scope: The proposal is focused, tackling specific AI techniques in a particular gaming context, which should enable meaningful experimental results.
* Innovative Use of LLMs: Utilizing LLMs in a tactical and strategic context is a novel approach that could reveal insights into AI-based decision-making.
* Detailed Methodology: The proposal outlines specific techniques such as supervised fine-tuning, KAG, and CoT, indicating a structured approach to improve the model’s capabilities.
* Clear Evaluation Metrics: The plan to measure win rates and strategic performance on Pokémon Showdown provides tangible, quantifiable metrics for evaluating the model.
# Cons
* Lack of Specificity in Implementation: The proposal could benefit from more detail on data handling and preprocessing, or how KAG and CoT will be implemented in practice.
* Limited Discussion on Limitations: There is little mention of potential limitations or challenges, such as computational resource demands or handling unpredictable human strategies.
* Risk of Overfitting to a Niche Context: Fine-tuning for Pokémon battles may yield results that are not easily generalizable to other interactive settings, limiting broader applicability.
* Complexity of Evaluation: Evaluating LLMs in dynamic games may pose challenges, particularly in assessing "strategic performance" without a clear set of benchmarks.
rating: 7
confidence: 4 |
SrREBjZwYY | 【Proposal】Pokémon Battle Agent based on LLMs | [
"Zihan Lv",
"Cen Qihang"
] | The rapid development of LLMs has led to widespread applications in interactive environments, particularly in gaming, where LLM agents demonstrate impressive decision-making and strategy execution capabilities. This paper focuses on developing a Pokémon battle agent based on LLMs. We combine techniques such as supervised fine-tuning, KAG and Self-Consistency to improve agent's contextual understanding and generate effective battle commands. Through experiments on Pokémon Showdown with robots and human, we will evaluate the model's win rates and strategic performance, aiming to contribute to the development of LLM agents capable of dynamic and complex environments. | [
"LLMs",
"SFT",
"Knowledge-Augmented Generation",
"Self-Consistency"
] | https://openreview.net/pdf?id=SrREBjZwYY | LJWL7d9RBw | official_review | 1,731,250,301,572 | SrREBjZwYY | [
"everyone"
] | [
"~Chentian_wei1"
] | title: This paper intriguingly integrates popular LLM methods for Pokémon battles, but the method's breadth and lack of definition could benefit from a clearer framework description.
review: The work presented in this paper is quite interesting, integrating several of the most popular LLM methods currently available and applying them to the specific task of Pokémon battles, with the definition and rules of the Pokémon game being very clear. However, the proposed method seems somewhat too broad and lacks clear definitions. Additionally, it might be beneficial to provide a slightly more detailed introduction and description of the overall framework.
rating: 7
confidence: 4 |
SrREBjZwYY | 【Proposal】Pokémon Battle Agent based on LLMs | [
"Zihan Lv",
"Cen Qihang"
] | The rapid development of LLMs has led to widespread applications in interactive environments, particularly in gaming, where LLM agents demonstrate impressive decision-making and strategy execution capabilities. This paper focuses on developing a Pokémon battle agent based on LLMs. We combine techniques such as supervised fine-tuning, KAG and Self-Consistency to improve agent's contextual understanding and generate effective battle commands. Through experiments on Pokémon Showdown with robots and human, we will evaluate the model's win rates and strategic performance, aiming to contribute to the development of LLM agents capable of dynamic and complex environments. | [
"LLMs",
"SFT",
"Knowledge-Augmented Generation",
"Self-Consistency"
] | https://openreview.net/pdf?id=SrREBjZwYY | 6uWHH9Mzog | official_review | 1,731,138,986,947 | SrREBjZwYY | [
"everyone"
] | [
"~Kangping_Xu1"
] | title: Review of "Pokémon Battle Agent based on LLMs"
review: ## Pros
- **Innovative Approach**: Leveraging LLMs to develop an interactive game agent demonstrates the potential of AI beyond conventional text-based tasks, particularly in dynamic and strategic environments like Pokémon battles.
- **Comprehensive Techniques**: The integration of KAG and Chain-of-Thought reasoning provides a strong foundation for improving contextual accuracy and decision-making, reducing model hallucinations.
## Cons
- **Dataset Limitations**: Relying solely on Pokémon Showdown battle data may introduce biases, limiting the model's adaptability to novel scenarios outside the dataset's scope, so I think online data collection and RL methods may help.
Overall, this project holds promise for advancing AI capabilities in gaming, but its success will depend on overcoming challenges related to data diversity and computational resource demands. By improving LLMs' contextual understanding in dynamic environments, this project could pave the way for more sophisticated AI-driven game interactions.
rating: 8
confidence: 4 |
SrREBjZwYY | 【Proposal】Pokémon Battle Agent based on LLMs | [
"Zihan Lv",
"Cen Qihang"
] | The rapid development of LLMs has led to widespread applications in interactive environments, particularly in gaming, where LLM agents demonstrate impressive decision-making and strategy execution capabilities. This paper focuses on developing a Pokémon battle agent based on LLMs. We combine techniques such as supervised fine-tuning, KAG and Self-Consistency to improve agent's contextual understanding and generate effective battle commands. Through experiments on Pokémon Showdown with robots and human, we will evaluate the model's win rates and strategic performance, aiming to contribute to the development of LLM agents capable of dynamic and complex environments. | [
"LLMs",
"SFT",
"Knowledge-Augmented Generation",
"Self-Consistency"
] | https://openreview.net/pdf?id=SrREBjZwYY | 3Y5DPbvnw7 | official_review | 1,731,413,777,264 | SrREBjZwYY | [
"everyone"
] | [
"~Gangxin_Xu1"
] | title: Review of "Pokémon Battle Agent based on LLMs"
review: This proposal introduces a large language model (LLM)-based agent designed for Pokémon battles, leveraging techniques like supervised fine-tuning (SFT), knowledge-augmented generation (KAG), and self-consistency. The goal is to create an agent with enhanced contextual understanding and strategic decision-making abilities. Evaluation will involve testing the agent's performance in Pokémon Showdown battles against both AI and human opponents, with a focus on win rates and strategic effectiveness.
Strengths:
Novel Application in Gaming: Developing an LLM agent specifically for Pokémon battles is an innovative application, as it combines complex decision-making, strategy, and adaptability in a well-defined environment.
Robust Methodology: The integration of SFT, KAG, and self-consistency reflects a well-rounded approach to enhancing the agent's contextual and strategic capabilities, addressing the nuances of battle decisions.
Clear Evaluation Plan: The proposal outlines a straightforward and relevant evaluation method through win rates and strategic assessment in Pokémon Showdown, providing concrete metrics to gauge the agent’s performance.
rating: 8
confidence: 4 |
SnQVA40uaA | [Proposal-ML] Global Wheat Spike Detection and Quantitative Analysis Based on Image Recognition | [
"Xun Wang",
"Tianhai Liang",
"Gu Zhang"
] | Wheat is one of the most important staple crops worldwide. Rapid and accurate detection of wheat spikes from outdoor field images is essential for farmers to implement efficient field management and enhance the quality of wheat cultivation. However, factors like planting environment and image quality complicate achieving robust and precise detection using visual models. Therefore, we propose a framework that employs adversarial training with diffusion models and YOLO-v11 visual models to address this challenge. | [
"Wheat spike detection; Object detection; Adversarial training; Diffusion model; YOLO-v11"
] | https://openreview.net/pdf?id=SnQVA40uaA | zY1BjGfA8Y | official_review | 1,731,426,835,945 | SnQVA40uaA | [
"everyone"
] | [
"~Chendong_Xiang1"
] | title: interest topic
review: This paper proposes a framework combining diffusion models and YOLO-v11 with adversarial training for global wheat spike detection and quantitative analysis. The approach enhances model generalization and robustness across diverse environments through data augmentation and adversarial sample generation. The authors plan to validate the framework on the Global Wheat Head Detection (GWHD) dataset, aiming to support effective wheat growth monitoring in agricultural production.
rating: 8
confidence: 2 |
SnQVA40uaA | [Proposal-ML] Global Wheat Spike Detection and Quantitative Analysis Based on Image Recognition | [
"Xun Wang",
"Tianhai Liang",
"Gu Zhang"
] | Wheat is one of the most important staple crops worldwide. Rapid and accurate detection of wheat spikes from outdoor field images is essential for farmers to implement efficient field management and enhance the quality of wheat cultivation. However, factors like planting environment and image quality complicate achieving robust and precise detection using visual models. Therefore, we propose a framework that employs adversarial training with diffusion models and YOLO-v11 visual models to address this challenge. | [
"Wheat spike detection; Object detection; Adversarial training; Diffusion model; YOLO-v11"
] | https://openreview.net/pdf?id=SnQVA40uaA | sV5L53qEHY | official_review | 1,731,258,917,482 | SnQVA40uaA | [
"everyone"
] | [
"~Matteo_Jiahao_Chen1"
] | title: Well-structured proposal fo Global Wheat Spike Detection and Quantitative Analysis
review: This work proposes a framework that combines diffusion-based data augmentation with YOLO-v11 in an adversarial training setup to improve wheat spike detection in field images.
### Strengths
- The combination of diffusion models with YOLO-v11 in adversarial training may improve detection robustness but it may depend on the quality of the generated images.
- The framework addresses real-world challenges benefiting agricultural management.
### Weaknesses
- A deeper analysis of model performance under varied field conditions would strengthen the study.
rating: 9
confidence: 4 |
SnQVA40uaA | [Proposal-ML] Global Wheat Spike Detection and Quantitative Analysis Based on Image Recognition | [
"Xun Wang",
"Tianhai Liang",
"Gu Zhang"
] | Wheat is one of the most important staple crops worldwide. Rapid and accurate detection of wheat spikes from outdoor field images is essential for farmers to implement efficient field management and enhance the quality of wheat cultivation. However, factors like planting environment and image quality complicate achieving robust and precise detection using visual models. Therefore, we propose a framework that employs adversarial training with diffusion models and YOLO-v11 visual models to address this challenge. | [
"Wheat spike detection; Object detection; Adversarial training; Diffusion model; YOLO-v11"
] | https://openreview.net/pdf?id=SnQVA40uaA | lya2v2abjf | official_review | 1,731,395,821,488 | SnQVA40uaA | [
"everyone"
] | [
"~Kaiyuan_Zhang6"
] | title: Good proposal
review: A clear proposed topic and relative fulfilled description on background, definition, related work and methods, and include technique details. Besides, the methods is clearly presented via a framework figure.
One possible challenge may be how to guarantee the augmentation data using diffusion model is still a wheat picture, which should be discussed in the future work.
rating: 9
confidence: 5 |
SnQVA40uaA | [Proposal-ML] Global Wheat Spike Detection and Quantitative Analysis Based on Image Recognition | [
"Xun Wang",
"Tianhai Liang",
"Gu Zhang"
] | Wheat is one of the most important staple crops worldwide. Rapid and accurate detection of wheat spikes from outdoor field images is essential for farmers to implement efficient field management and enhance the quality of wheat cultivation. However, factors like planting environment and image quality complicate achieving robust and precise detection using visual models. Therefore, we propose a framework that employs adversarial training with diffusion models and YOLO-v11 visual models to address this challenge. | [
"Wheat spike detection; Object detection; Adversarial training; Diffusion model; YOLO-v11"
] | https://openreview.net/pdf?id=SnQVA40uaA | iwZFLFmmHI | official_review | 1,731,169,890,847 | SnQVA40uaA | [
"everyone"
] | [
"~Bryan_Constantine_Sadihin1"
] | title: Review of "Global Wheat Spike Detection and Quantitative Analysis Based on Image Recognition"
review: Strength:
1. High Relevance and Potential Impact: The proposal offers a solution for improving wheat production, which is important for global food security.
2. Use of advanced method: The proposal clearly defines the underrepresented class problem in wheat detection, and proposes uses generated augmented samples.
rating: 10
confidence: 5 |
SnQVA40uaA | [Proposal-ML] Global Wheat Spike Detection and Quantitative Analysis Based on Image Recognition | [
"Xun Wang",
"Tianhai Liang",
"Gu Zhang"
] | Wheat is one of the most important staple crops worldwide. Rapid and accurate detection of wheat spikes from outdoor field images is essential for farmers to implement efficient field management and enhance the quality of wheat cultivation. However, factors like planting environment and image quality complicate achieving robust and precise detection using visual models. Therefore, we propose a framework that employs adversarial training with diffusion models and YOLO-v11 visual models to address this challenge. | [
"Wheat spike detection; Object detection; Adversarial training; Diffusion model; YOLO-v11"
] | https://openreview.net/pdf?id=SnQVA40uaA | iTsuxZA6XG | official_review | 1,731,392,677,336 | SnQVA40uaA | [
"everyone"
] | [
"~Qihang_Cen1"
] | title: Practical research problem, clear technical framework
review: This paper focuses on global wheat spike detection and quantitative analysis, which is a well pratical problem and has significant implications for global food security. Furthermore, the study offers a technique framework using GANs combined with YOLO and diffusion models for data augmentation, with adversarial training to increase the robustness. This framework is innocative and well-structured, with clear details about model and experiment present in proposal. Additionally, maybe append discussion the advantages of model selection could provide readers with more clarity on its suitability.
rating: 9
confidence: 4 |
SnQVA40uaA | [Proposal-ML] Global Wheat Spike Detection and Quantitative Analysis Based on Image Recognition | [
"Xun Wang",
"Tianhai Liang",
"Gu Zhang"
] | Wheat is one of the most important staple crops worldwide. Rapid and accurate detection of wheat spikes from outdoor field images is essential for farmers to implement efficient field management and enhance the quality of wheat cultivation. However, factors like planting environment and image quality complicate achieving robust and precise detection using visual models. Therefore, we propose a framework that employs adversarial training with diffusion models and YOLO-v11 visual models to address this challenge. | [
"Wheat spike detection; Object detection; Adversarial training; Diffusion model; YOLO-v11"
] | https://openreview.net/pdf?id=SnQVA40uaA | bq05kr25TN | official_review | 1,731,417,057,102 | SnQVA40uaA | [
"everyone"
] | [
"~Bowen_Gao1"
] | title: Review of Global Wheat Spike Detection and Quantitative Analysis Based on Image Recognition
review: **Summary**
This proposal addresses the Global Wheat Spike Detection task, where the authors propose using adversarial training to tackle challenges related to diverse planting environments and varying image quality.
**Strengths**
1. The problem definition and proposed method are clearly stated, supported by mathematical formulations that enhance clarity and rigor.
2. The review of related work is comprehensive
3. The inclusion of a framework figure effectively illustrates the entire pipeline, making it easier to understand the methodology and workflow.
**Weaknesses**
1. The explanation of challenges in previous methods could be expanded, providing more insight into the specific limitations and issues that the proposed approach aims to overcome.
rating: 9
confidence: 4 |
SnQVA40uaA | [Proposal-ML] Global Wheat Spike Detection and Quantitative Analysis Based on Image Recognition | [
"Xun Wang",
"Tianhai Liang",
"Gu Zhang"
] | Wheat is one of the most important staple crops worldwide. Rapid and accurate detection of wheat spikes from outdoor field images is essential for farmers to implement efficient field management and enhance the quality of wheat cultivation. However, factors like planting environment and image quality complicate achieving robust and precise detection using visual models. Therefore, we propose a framework that employs adversarial training with diffusion models and YOLO-v11 visual models to address this challenge. | [
"Wheat spike detection; Object detection; Adversarial training; Diffusion model; YOLO-v11"
] | https://openreview.net/pdf?id=SnQVA40uaA | MOSwBa21F9 | official_review | 1,731,035,266,649 | SnQVA40uaA | [
"everyone"
] | [
"~Lily_Sheng1"
] | title: Submission 39 Review
review: This work proposes an advanced wheat spike detection approach by leveraging a combination of generative models for data augmentation and a YOLO-based object detection. The method incorporates adversarial training to generate challenging augmented samples to improve detection accuracy and generalization.
Pros:
1. There are clear approaches and baselines defined.
2. Using a diffusion model for data augmentation helps to improve the model's robustness and generalizability.
3. This work aligns well with the practical needs of agricultural monitoring.
Cons:
1. This approach may heavily depend on the quality of generated images, which can vary depending on the diffusion model's capability to accurately replicate real-world visual diversity.
rating: 10
confidence: 4 |
SnQVA40uaA | [Proposal-ML] Global Wheat Spike Detection and Quantitative Analysis Based on Image Recognition | [
"Xun Wang",
"Tianhai Liang",
"Gu Zhang"
] | Wheat is one of the most important staple crops worldwide. Rapid and accurate detection of wheat spikes from outdoor field images is essential for farmers to implement efficient field management and enhance the quality of wheat cultivation. However, factors like planting environment and image quality complicate achieving robust and precise detection using visual models. Therefore, we propose a framework that employs adversarial training with diffusion models and YOLO-v11 visual models to address this challenge. | [
"Wheat spike detection; Object detection; Adversarial training; Diffusion model; YOLO-v11"
] | https://openreview.net/pdf?id=SnQVA40uaA | EwlZQfN5GU | official_review | 1,731,401,673,415 | SnQVA40uaA | [
"everyone"
] | [
"~Zhixuan_Pan1"
] | title: Review
review: This project aims to improve global wheat spike detection using image recognition. By combining adversarial training with diffusion models and the YOLO-v11 architecture, the project seeks to achieve accurate and robust detection across diverse environmental conditions.
Pros:
1. Combining diffusion models and YOLO-v11 for adversarial training framework like GAN is a novel method.
2. The proposal's objectives are specific. The methods presented are concrete and detailed.
Cons:
1. More ablation studies may be needed, such as conducting experiments using only the diffusion model for data augmentation without adversarial training.
2. There is limited novelty in the methodology. It is similar to many previous works in image classification.
rating: 9
confidence: 4 |
SnQVA40uaA | [Proposal-ML] Global Wheat Spike Detection and Quantitative Analysis Based on Image Recognition | [
"Xun Wang",
"Tianhai Liang",
"Gu Zhang"
] | Wheat is one of the most important staple crops worldwide. Rapid and accurate detection of wheat spikes from outdoor field images is essential for farmers to implement efficient field management and enhance the quality of wheat cultivation. However, factors like planting environment and image quality complicate achieving robust and precise detection using visual models. Therefore, we propose a framework that employs adversarial training with diffusion models and YOLO-v11 visual models to address this challenge. | [
"Wheat spike detection; Object detection; Adversarial training; Diffusion model; YOLO-v11"
] | https://openreview.net/pdf?id=SnQVA40uaA | 6EXPhgmTrR | official_review | 1,730,891,772,118 | SnQVA40uaA | [
"everyone"
] | [
"~Shaoting_Zhu1"
] | title: Review of submission 39
review: The proposal presents a framework aimed at detecting and quantifying wheat spikes from outdoor field images using advanced image recognition techniques. This is a critical task for agricultural management, as it can aid in optimizing wheat production and enhancing the quality of cultivation. The authors propose a sophisticated approach that combines adversarial training with diffusion models and YOLO-v11 visual models to achieve robust and precise detection under varying conditions.
**Stength**
1. Innovative Framework: The integration of adversarial training with diffusion models and YOLO-v11 is a novel approach that could potentially enhance the robustness and accuracy of wheat spike detection.
2. Clear task and metrics: The authors plan to evaluate the model using the Intersection over Union (IoU) metric and following Kaggle competition guidelines. The **Definition** paragraph clearly defines the task. The dataset and pre-trained model are clearly surveyed in the proposal, making the project very feasible.
**Weakness**
To train a diffusion model and a yolov11 model together may cost a lot of computation resources.
Overall, this proposal is well-written, and the task, dataset, and method are very clear.
rating: 10
confidence: 4 |
SnQVA40uaA | [Proposal-ML] Global Wheat Spike Detection and Quantitative Analysis Based on Image Recognition | [
"Xun Wang",
"Tianhai Liang",
"Gu Zhang"
] | Wheat is one of the most important staple crops worldwide. Rapid and accurate detection of wheat spikes from outdoor field images is essential for farmers to implement efficient field management and enhance the quality of wheat cultivation. However, factors like planting environment and image quality complicate achieving robust and precise detection using visual models. Therefore, we propose a framework that employs adversarial training with diffusion models and YOLO-v11 visual models to address this challenge. | [
"Wheat spike detection; Object detection; Adversarial training; Diffusion model; YOLO-v11"
] | https://openreview.net/pdf?id=SnQVA40uaA | 575TYAUZY0 | official_review | 1,731,416,069,911 | SnQVA40uaA | [
"everyone"
] | [
"~Yuji_Wang4"
] | title: Review of "Global Wheat Spike Detection and Quantitative Analysis Based on Image Recognition"
review: The project focuses on the problem of global wheat spike detection. The authors model the problem as image recognition and propose to solve global wheat spike detection problems with a combination of diffusion models and object detection models, which will be trained with an adversarial method.
### Strengths:
1. Method design: The proposal offers a framework that leverages generative models (diffusion models) to improve object recognition accuracy.
2. Feasibility：The research problem is well-defined, and related works are thoroughly discussed. The experimental plan is concrete, making the project feasible.
### Weaknesses:
1. Clarity of writing: The proposal lacks specific details on how to integrate diffusion models and adversarial training with object recognition models.
Concerns: Is there evidence (e.g., difficulty of the problem, limitations of existing methods) to justify the need of applying the sophisticated pipeline to the task? Will the use of adversarial training introduce training instability?
rating: 9
confidence: 3 |
RlPTe6nUao | [Proposal-ML] LLM Hallucination Detection: Fine-Tuning Gemma2 | [
"Ziyi Liu",
"Rosalie Butte"
] | Large Language Models (LLMs) have become increasingly important recently in our daily lifes. However, these models can sometimes give false or misleading answers, called hallucinations. Therefore, it is important to detect these hallucinations in the generated text of LLM. In this project, we propose a method to detect these hallucinations. | [
"LLM",
"LLM hallucinations",
"detection"
] | https://openreview.net/pdf?id=RlPTe6nUao | x3jjywrd3C | official_review | 1,731,114,532,673 | RlPTe6nUao | [
"everyone"
] | [
"~Xiying_Huang2"
] | title: Fine-Tuning Gemma2 for Effective Hallucination Detection in LLMs
review: This proposal presents an innovative approach to detect hallucinations in large language models (LLMs) by fine-tuning the Gemma2 model. The project builds on recent advancements in hallucination detection, aiming to classify LLM-generated responses as either hallucinated or accurate using a binary classification framework. The methodology involves leveraging Chain-of-Thought (CoT) reasoning to split prompt-response pairs into intermediate question-answer segments, improving detection precision. The paper is clear, technically sound, and well-grounded in relevant literature, presenting meaningful insights into a critical area of NLP.
Pros:
• Addresses a significant and timely issue in LLM development.
• Incorporates a structured approach using CoT and Gemma2 fine-tuning.
• Compares accuracy with other established hallucination detection models.
Cons:
• Lack of clarity on evaluation metrics beyond accuracy.
• Potential challenges in CoT application for varied LLM prompt-response formats.
rating: 9
confidence: 4 |
RlPTe6nUao | [Proposal-ML] LLM Hallucination Detection: Fine-Tuning Gemma2 | [
"Ziyi Liu",
"Rosalie Butte"
] | Large Language Models (LLMs) have become increasingly important recently in our daily lifes. However, these models can sometimes give false or misleading answers, called hallucinations. Therefore, it is important to detect these hallucinations in the generated text of LLM. In this project, we propose a method to detect these hallucinations. | [
"LLM",
"LLM hallucinations",
"detection"
] | https://openreview.net/pdf?id=RlPTe6nUao | wPVxbQ0CDN | official_review | 1,731,416,457,436 | RlPTe6nUao | [
"everyone"
] | [
"~Wuqian1"
] | title: Review of "LLM Hallucination Detection: Fine-Tuning Gemma2"
review: The proposal "LLM Hallucination Detection: Fine-Tuning Gemma2" addresses a critical issue in the field of natural language processing—the problem of hallucinations in Large Language Models (LLMs),The proposal is not given a clear methodology for training and evaluating the model.
Pros
1.Relevance: The project addresses a current and pressing issue in AI, making it highly relevant.
Cons
1.Unproven Effectiveness: The effectiveness of fine-tuning Gemma2 for hallucination detection is yet to be proven.
2.Methodical:The proposal is not given a clear methodology for training and evaluating the model.
3.Lack of Experimental Results: As a proposal, it lacks experimental results to support the claimed potential of the method.
rating: 5
confidence: 3 |
RlPTe6nUao | [Proposal-ML] LLM Hallucination Detection: Fine-Tuning Gemma2 | [
"Ziyi Liu",
"Rosalie Butte"
] | Large Language Models (LLMs) have become increasingly important recently in our daily lifes. However, these models can sometimes give false or misleading answers, called hallucinations. Therefore, it is important to detect these hallucinations in the generated text of LLM. In this project, we propose a method to detect these hallucinations. | [
"LLM",
"LLM hallucinations",
"detection"
] | https://openreview.net/pdf?id=RlPTe6nUao | jpsWgsnUbR | official_review | 1,731,308,649,717 | RlPTe6nUao | [
"everyone"
] | [
"~Fabian_Pawelczyk1"
] | title: Important Topic, Clear Introduction but lack in Methodology
review: # Decision: Clear Accept
## Strengths
- **Problem Significance**: This paper addresses hallucination detection in Large Language Models (LLMs), a critical and timely issue given the increasing use of LLMs in applications that require high factual accuracy.
- **Clarity and Organization**: The paper is clearly organized, with well-defined sections for problem description, methodology, and related work, making it easy to follow.
## Areas for Improvement
- **Problem Definition**: While the introduction discusses the importance of hallucination detection, it could go deeper into explaining why hallucination is a particularly challenging problem.
- **Pipeline Details**: Providing more specifics on the fine-tuning approach for Gemma2 would enhance the methodology section and clarify the technical contributions.
---
Overall, this is a strong and well-prepared proposal addressing an important topic.
rating: 8
confidence: 4 |
RlPTe6nUao | [Proposal-ML] LLM Hallucination Detection: Fine-Tuning Gemma2 | [
"Ziyi Liu",
"Rosalie Butte"
] | Large Language Models (LLMs) have become increasingly important recently in our daily lifes. However, these models can sometimes give false or misleading answers, called hallucinations. Therefore, it is important to detect these hallucinations in the generated text of LLM. In this project, we propose a method to detect these hallucinations. | [
"LLM",
"LLM hallucinations",
"detection"
] | https://openreview.net/pdf?id=RlPTe6nUao | e6VlAvqovH | official_review | 1,731,140,052,210 | RlPTe6nUao | [
"everyone"
] | [
"~Yuanda_Zhang1"
] | title: review for proposal 27
review: The proposal presents a focused effort on detecting and mitigating hallucinations in Large Language Models (LLMs) by fine-tuning the Gemma2 model. The project aims to address the critical issue of inaccuracies in LLM outputs, which can lead to significant consequences.
Pros:
1）The project's relevance is highlighted by the increasing dependence on LLMs and the pressing need for reliability in their outputs.
2）The proposal's approach to use Gemma2, a well-established model, as a foundation for fine-tuning shows promise in improving detection capabilities.
3）The binary classification task definition is clear and provides a straightforward framework for evaluating the model's performance.
Cons:
1）The proposal could benefit from a more detailed explanation of how the model will handle the nuances of different types of hallucinations, beyond just identifying them.
2）While the project plans to compare its model with established ones, there is a lack of preliminary results or benchmarks to suggest the expected improvement over current methods.
rating: 8
confidence: 4 |
RlPTe6nUao | [Proposal-ML] LLM Hallucination Detection: Fine-Tuning Gemma2 | [
"Ziyi Liu",
"Rosalie Butte"
] | Large Language Models (LLMs) have become increasingly important recently in our daily lifes. However, these models can sometimes give false or misleading answers, called hallucinations. Therefore, it is important to detect these hallucinations in the generated text of LLM. In this project, we propose a method to detect these hallucinations. | [
"LLM",
"LLM hallucinations",
"detection"
] | https://openreview.net/pdf?id=RlPTe6nUao | e2KVxaN7N8 | official_review | 1,731,379,638,448 | RlPTe6nUao | [
"everyone"
] | [
"~Killian_Conyngham1"
] | title: Review of LLM Hallucination Detection: Fine-Tuning Gemma2
review: Overall this is a strong and well-structured proposal.
The introduction provides a clear overview of the problem of Hallucinations, its importance and relevancy. The problem description section expands on this well by clearly introducing the exact classification task involved and the proposed technique of using Chain-of-Thought (CoT). It would be useful to have a more detailed description of how exactly you plan on fine-tuning Gemma2, and to go into more detail in which ways you plan to expand or improve on existing CoT-based detectors. The related work section gives an insightful and detailed overview of the relevant literature. One suggestion would also be to perhaps also compare the hallucination detection accuracy to other non-LLM based approaches, or even manual review for a smaller subset of the data as another reference point.
rating: 8
confidence: 4 |
RlPTe6nUao | [Proposal-ML] LLM Hallucination Detection: Fine-Tuning Gemma2 | [
"Ziyi Liu",
"Rosalie Butte"
] | Large Language Models (LLMs) have become increasingly important recently in our daily lifes. However, these models can sometimes give false or misleading answers, called hallucinations. Therefore, it is important to detect these hallucinations in the generated text of LLM. In this project, we propose a method to detect these hallucinations. | [
"LLM",
"LLM hallucinations",
"detection"
] | https://openreview.net/pdf?id=RlPTe6nUao | duO3yV0WTt | official_review | 1,731,203,940,917 | RlPTe6nUao | [
"everyone"
] | [
"~Renrui_Tian1"
] | title: Clear Problem Definition, but Methodology and Challenges Need More Specificity
review: **Strengths**:
* **Clear Problem Definition**: The proposal effectively defines the problem of LLM hallucination detection and its significance. It highlights the risks associated with hallucinations and the need for accurate detection methods.
* **Related Work**: The proposal includes a comprehensive survey on the relevant techniques, demonstrating a good understanding of the existing literature on LLM hallucination detection.
**Areas for Improvement**:
* **Detailed Fine-tuning Approach**: The proposal could benefit from a more detailed description of the fine-tuning process. This includes specifics about the training regimen, hyperparameter tuning, and the use of Chain-of-Thought (CoT) for splitting prompts and responses.
* **Challenges**: The proposal falls short in presenting the potential challenges to overcome when training a binary classification model to detect hallucination.
**Overall, this proposal presents a well-structured and promising approach to LLM hallucination detection. Addressing the suggested areas for improvement would further strengthen the proposal and increase the likelihood of successful implementation**.
rating: 7
confidence: 3 |
RlPTe6nUao | [Proposal-ML] LLM Hallucination Detection: Fine-Tuning Gemma2 | [
"Ziyi Liu",
"Rosalie Butte"
] | Large Language Models (LLMs) have become increasingly important recently in our daily lifes. However, these models can sometimes give false or misleading answers, called hallucinations. Therefore, it is important to detect these hallucinations in the generated text of LLM. In this project, we propose a method to detect these hallucinations. | [
"LLM",
"LLM hallucinations",
"detection"
] | https://openreview.net/pdf?id=RlPTe6nUao | dEF55pwNRx | official_review | 1,731,327,578,292 | RlPTe6nUao | [
"everyone"
] | [
"~Cheng_Gao2"
] | title: Review for LLM Hallucination Detection: Fine-Tuning Gemma2
review: Strengths:
- Clear task definition.
- Hallucination detection is a research hotspot and holds great importance.
Weaknesses:
- The proposed method seems a little bit simple. I think the trained Gemma2 model may rely heavily on its pre-existing knowledge to judge the accuracy of a response, rather than learning new knowledge from the approximately 10,000 training samples. Thus, I am concerned that the trained Gemma2 may not perform significantly better than an untrained version.
- The proposal could benefit from a more detailed description of how the language model is adapted into a binary classification model. Slight architectural adjustments at the language model head may be necessary.
rating: 8
confidence: 4 |
RlPTe6nUao | [Proposal-ML] LLM Hallucination Detection: Fine-Tuning Gemma2 | [
"Ziyi Liu",
"Rosalie Butte"
] | Large Language Models (LLMs) have become increasingly important recently in our daily lifes. However, these models can sometimes give false or misleading answers, called hallucinations. Therefore, it is important to detect these hallucinations in the generated text of LLM. In this project, we propose a method to detect these hallucinations. | [
"LLM",
"LLM hallucinations",
"detection"
] | https://openreview.net/pdf?id=RlPTe6nUao | bES3gz6G1c | official_review | 1,731,407,282,151 | RlPTe6nUao | [
"everyone"
] | [
"~Jackson_M_Luckey1"
] | title: Proposal Review
review: The proposal does a good job of explaining their interpretation of LLM "hallucinations". While I do not agree with the entirety of the choosen definition, clearly explaining the definiton makes the paper much easier to evaluate. Using a Kaggle dataset with labelled hallucinations is a great approach.
The related work section provides a good overview of the existing literature.
I would like to know more about the methodology you plan on using. I found the proposal sparse on technical details, but the 2 page cap is obviously a limiting factor. I am particularly interested in what chain-of-thought approach you plan on going with.
rating: 8
confidence: 4 |
RlPTe6nUao | [Proposal-ML] LLM Hallucination Detection: Fine-Tuning Gemma2 | [
"Ziyi Liu",
"Rosalie Butte"
] | Large Language Models (LLMs) have become increasingly important recently in our daily lifes. However, these models can sometimes give false or misleading answers, called hallucinations. Therefore, it is important to detect these hallucinations in the generated text of LLM. In this project, we propose a method to detect these hallucinations. | [
"LLM",
"LLM hallucinations",
"detection"
] | https://openreview.net/pdf?id=RlPTe6nUao | aIpwZqqbp4 | official_review | 1,731,142,295,103 | RlPTe6nUao | [
"everyone"
] | [
"~Tim_Bakkenes1"
] | title: Good proposal
review: This is a good proposal.
- The background is relevant and motivates the need for your research well.
- The problem description is and it is good that you describe the datasets you will be using and give a formal problem definition.
- You provide some examples of other models that will be used to evaluate the performance of your model but more motivation for your choice of models and some information about how the comparison would work would improve your proposal.
- The related work is relevant and could have been used to come up with more ideas on your method.
- While the related work section is good, it would have been nice if you used that research to come up with a method tailored to your competition for achieving high accuracy.
rating: 8
confidence: 3 |
RlPTe6nUao | [Proposal-ML] LLM Hallucination Detection: Fine-Tuning Gemma2 | [
"Ziyi Liu",
"Rosalie Butte"
] | Large Language Models (LLMs) have become increasingly important recently in our daily lifes. However, these models can sometimes give false or misleading answers, called hallucinations. Therefore, it is important to detect these hallucinations in the generated text of LLM. In this project, we propose a method to detect these hallucinations. | [
"LLM",
"LLM hallucinations",
"detection"
] | https://openreview.net/pdf?id=RlPTe6nUao | FLNuP0JOyE | official_review | 1,731,409,726,500 | RlPTe6nUao | [
"everyone"
] | [
"~Han-Xi_Zhu1"
] | title: Review of LLM Hallucination Detection: Fine-Tuning Gemma2
review: This work gives a clear proposal on their topic "Hallucination Detection" with their well organized writing. The authors offer easy-understanding interpretion on the focused problem and give detailed and solid related literature on their topic.
Their proposed approach is easy to follow. What I concern is if there are any other works dealing with the same problem and how they perform on the specific task. And the authors may have to clarify the reason why they choose Gemma2.
Thank you!
rating: 8
confidence: 4 |
RlPTe6nUao | [Proposal-ML] LLM Hallucination Detection: Fine-Tuning Gemma2 | [
"Ziyi Liu",
"Rosalie Butte"
] | Large Language Models (LLMs) have become increasingly important recently in our daily lifes. However, these models can sometimes give false or misleading answers, called hallucinations. Therefore, it is important to detect these hallucinations in the generated text of LLM. In this project, we propose a method to detect these hallucinations. | [
"LLM",
"LLM hallucinations",
"detection"
] | https://openreview.net/pdf?id=RlPTe6nUao | F83EEGFNHv | official_review | 1,731,138,400,841 | RlPTe6nUao | [
"everyone"
] | [
"~Jinsong_Xiao1"
] | title: review for proposal 27
review: The paper proposes a method to detect hallucinations in large language models (LLMs) by fine-tuning the Gemma2 model with Chain-of-Thought (CoT) reasoning. The method targets a binary classification problem to distinguish between hallucinated and factual responses, with comparisons planned against established models like SelfCheckGPT and ChatProtect. This work is situated within the growing field of LLM reliability.
strength:
- Relevance: Addresses a timely problem as LLMs see increased deployment.
- Clarity: Well-organized with a clear approach and solid literature review.
- Adequate research on related work.
Weakness
- As a binary classification problem seems to be something unnovel, despite the use of large model fine-tuning and CoT and other technologies.
rating: 8
confidence: 4 |
RlPTe6nUao | [Proposal-ML] LLM Hallucination Detection: Fine-Tuning Gemma2 | [
"Ziyi Liu",
"Rosalie Butte"
] | Large Language Models (LLMs) have become increasingly important recently in our daily lifes. However, these models can sometimes give false or misleading answers, called hallucinations. Therefore, it is important to detect these hallucinations in the generated text of LLM. In this project, we propose a method to detect these hallucinations. | [
"LLM",
"LLM hallucinations",
"detection"
] | https://openreview.net/pdf?id=RlPTe6nUao | 5ZBJRKlIZa | official_review | 1,731,340,580,007 | RlPTe6nUao | [
"everyone"
] | [
"~Ruowen_Zhao1"
] | title: Review on LLM Hallucination Detection: Fine-Tuning Gemma2
review: **Summary**
The project focuses on the Kaggle competition "ML Olympiad - Detect hallucinations in LLMs," where the goal is to classify language model responses as hallucinations or correct answers. The authors aim to apply a Chain-of-Thought (CoT) approach to split prompt-response pairs and fine-tune the Gemma2 model for this binary classification task.
**Strengths:**
The approach is presented with good clarity, outlining the dataset, problem definition, and methodology in a structured manner. The related work section is well-developed and clearly articulated, providing a strong foundation for the proposed approach.
**Weakness:**
+ Analysis on model selection: While the authors plan to fine-tune Gemma2, there is limited explanation or justification for selecting this specific model. More detail on why Gemma2 is well-suited for hallucination detection should be provided.
+ Lack of fine-tuning details: There is no specific explanation of how the model will be fine-tuned, such as hyperparameters, optimization strategy or any adjustments to the model architecture.
+ Lack of evaluation metrics: The approach mentions fine-tuning Gemma2 but does not provide a detailed plan for evaluating the model's performance.
rating: 7
confidence: 4 |
R5FR5H1fAk | Bridging the Sim-to-Real Gap for Efficient and Robust Robotic Skill Acquisition | [
"Yutao Ouyang",
"Jingzhi Cui"
] | In recent years, the integration of learning-based methods, particularly Imitation Learning (IL) and Reinforcement Learning (RL), has brought significant advancements to robotics applications. These methods enable robots to learn complex behaviors, with IL facilitating rapid skill acquisition via expert demonstrations and RL permitting self-discovery of optimal strategies. However, the reliance on real-world data presents substantial challenges, as data collection can be resource-intensive, time-consuming, and risky for robotic systems. While simulation environments have emerged as a practical solution, providing abundant training data, they also introduce the sim-to-real gap—a critical challenge that hampers the effective transfer of learned behaviors from simulations to real-world scenarios due to discrepancies in sensor performance, environmental conditions, and material properties.
To address these challenges, we propose a novel framework that combines the strengths of both IL and RL while minimizing transfer difficulties associated with simulation-trained models. Our approach leverages the cost-effectiveness of simulated data to enhance robot learning outcomes, utilizing advanced techniques to improve transferability and reduce the sim-to-real gap. By harmonizing the efficiency of IL with the autonomy of RL, we aim to create a more effective learning paradigm that not only accelerates skill acquisition but also enhances real-world applicability. Our results demonstrate that this integrated approach can significantly improve performance in robotic tasks, paving the way for more autonomous and capable robotic systems. | [
"Robotics",
"Sim-to-Real",
"RL",
"IL"
] | https://openreview.net/pdf?id=R5FR5H1fAk | zOWlzrUGQG | official_review | 1,731,225,313,045 | R5FR5H1fAk | [
"everyone"
] | [
"~Xun_Wang10"
] | title: Review for "Bridging the Sim-to-Real Gap for Efficient and Robust Robotic Skill Acquisition"
review: This proposal presents a novel architecture combining Imitation Learning (IL) and Reinforcement Learning (RL) to address the sim-to-real gap in robotics. This architecture leverages the advantages of both learning paradigms, enabling efficient skill acquisition in simulators and robust deployment in real-world settings.
Strength: The proposal thoroughly elaborates on the importance of the sim-to-real gap issue in robotics and provides a relatively comprehensive literature review.
Weakness: In the methodology section, the description of how IL and RL are combined may lack sufficient detail. Additionally, there is little mention of how the proposed framework will be evaluated.
rating: 9
confidence: 4 |
R5FR5H1fAk | Bridging the Sim-to-Real Gap for Efficient and Robust Robotic Skill Acquisition | [
"Yutao Ouyang",
"Jingzhi Cui"
] | In recent years, the integration of learning-based methods, particularly Imitation Learning (IL) and Reinforcement Learning (RL), has brought significant advancements to robotics applications. These methods enable robots to learn complex behaviors, with IL facilitating rapid skill acquisition via expert demonstrations and RL permitting self-discovery of optimal strategies. However, the reliance on real-world data presents substantial challenges, as data collection can be resource-intensive, time-consuming, and risky for robotic systems. While simulation environments have emerged as a practical solution, providing abundant training data, they also introduce the sim-to-real gap—a critical challenge that hampers the effective transfer of learned behaviors from simulations to real-world scenarios due to discrepancies in sensor performance, environmental conditions, and material properties.
To address these challenges, we propose a novel framework that combines the strengths of both IL and RL while minimizing transfer difficulties associated with simulation-trained models. Our approach leverages the cost-effectiveness of simulated data to enhance robot learning outcomes, utilizing advanced techniques to improve transferability and reduce the sim-to-real gap. By harmonizing the efficiency of IL with the autonomy of RL, we aim to create a more effective learning paradigm that not only accelerates skill acquisition but also enhances real-world applicability. Our results demonstrate that this integrated approach can significantly improve performance in robotic tasks, paving the way for more autonomous and capable robotic systems. | [
"Robotics",
"Sim-to-Real",
"RL",
"IL"
] | https://openreview.net/pdf?id=R5FR5H1fAk | wDzBrzvr4X | official_review | 1,731,142,046,656 | R5FR5H1fAk | [
"everyone"
] | [
"~Ziang_Zheng1"
] | title: a promising approach to bridging the sim-to-real gap for robotic skill acquisition
review: **Reviewer Response**
The paper presents a promising approach to bridging the sim-to-real gap for robotic skill acquisition using a framework that combines Imitation Learning (IL) and Reinforcement Learning (RL) to achieve efficient, robust skill transfer from simulation to the real world. By exclusively using RGB images and simulated data, the authors address the challenges of resource-intense, risky real-world training and aim to maximize transferability and real-world performance. The approach is well-motivated and relevant, especially given the need for scalable, safe training methods in robotics.
### Strengths
1. **Clear Problem Identification**: The paper effectively outlines the challenges of sim-to-real transfer and highlights the complementary strengths of IL and RL in addressing these issues.
2. **Use of Simulation-Only Data**: Focusing exclusively on simulation-trained models without any real-world data presents a cost-effective and safe training paradigm that could be impactful for many real-world applications. This approach is particularly beneficial for tasks with high real-world setup costs or operational risks.
3. **Integrative Methodology**: The integration of IL for rapid skill acquisition with RL’s robustness in unstructured environments is well-conceived. The emphasis on domain standardization across visual inputs to reduce discrepancies between simulation and real-world conditions shows strong insight into sim-to-real challenges.
### Areas for Improvement
1. **Experimental Results and Real-World Validation**: The paper would benefit significantly from real-world validation of the proposed framework’s effectiveness. Providing quantitative metrics for success rates, transfer efficiency, and any limitations observed in preliminary tests would strengthen the overall contribution.
2. **Comparative Analysis with Existing Methods**: Although the paper mentions domain randomization, adaptation, and real-to-sim techniques, it lacks a detailed comparison of the proposed method's performance with these established techniques. A side-by-side evaluation could highlight the unique advantages of the proposed approach.
3. **Specifics on Visual Domain Standardization**: The process of translating real-world images into a standardized simulation style for consistency is an interesting and novel component. However, more details on the technical aspects of this transformation and its impact on transfer performance would enhance the clarity and rigor of the paper.
4. **Adaptability and Scalability**: The framework's adaptability to various robotic tasks beyond grasping, as well as its scalability to multi-robot setups, would be valuable areas to address. Providing insights into how adaptable and scalable this framework is would increase its relevance to a broader audience.
5. **Handling of Noise and Variability in Simulated Environments**: Although domain standardization aims to address sim-to-real discrepancies, it is important to discuss the handling of potential noise and variability within the simulated environments themselves. Clarifying this could provide insights into the robustness of the trained policies.
### Recommendation
The paper provides a solid foundation for bridging the sim-to-real gap through a simulation-based, IL-RL framework. Addressing the above concerns—particularly around experimental validation and comparative analysis—would strengthen the contribution and applicability of the framework. I recommend acceptance with major revisions, especially with an emphasis on real-world performance metrics and detailed technical explanations of the standardization approach.
**Suggested Action**: Accept with Major Revisions
rating: 8
confidence: 3 |
R5FR5H1fAk | Bridging the Sim-to-Real Gap for Efficient and Robust Robotic Skill Acquisition | [
"Yutao Ouyang",
"Jingzhi Cui"
] | In recent years, the integration of learning-based methods, particularly Imitation Learning (IL) and Reinforcement Learning (RL), has brought significant advancements to robotics applications. These methods enable robots to learn complex behaviors, with IL facilitating rapid skill acquisition via expert demonstrations and RL permitting self-discovery of optimal strategies. However, the reliance on real-world data presents substantial challenges, as data collection can be resource-intensive, time-consuming, and risky for robotic systems. While simulation environments have emerged as a practical solution, providing abundant training data, they also introduce the sim-to-real gap—a critical challenge that hampers the effective transfer of learned behaviors from simulations to real-world scenarios due to discrepancies in sensor performance, environmental conditions, and material properties.
To address these challenges, we propose a novel framework that combines the strengths of both IL and RL while minimizing transfer difficulties associated with simulation-trained models. Our approach leverages the cost-effectiveness of simulated data to enhance robot learning outcomes, utilizing advanced techniques to improve transferability and reduce the sim-to-real gap. By harmonizing the efficiency of IL with the autonomy of RL, we aim to create a more effective learning paradigm that not only accelerates skill acquisition but also enhances real-world applicability. Our results demonstrate that this integrated approach can significantly improve performance in robotic tasks, paving the way for more autonomous and capable robotic systems. | [
"Robotics",
"Sim-to-Real",
"RL",
"IL"
] | https://openreview.net/pdf?id=R5FR5H1fAk | vfmEd7oaQt | official_review | 1,731,415,701,886 | R5FR5H1fAk | [
"everyone"
] | [
"~Kittaphot_Saengprachathanarak1"
] | title: Review of "Bridging the Sim-to-Real Gap for Efficient and Robust Robotic Skill Acquisition"
review: This proposal presents a strong and relevant approach to bridging the sim-to-real gap in robotic skill acquisition. By combining Imitation Learning (IL) and Reinforcement Learning (RL), it aims to create a framework that enhances the efficiency of skill acquisition while ensuring high transferability to real-world environments. The authors provide a detailed discussion of the limitations of existing methods, such as the high cost and risk of real-world data collection, and address the sim-to-real gap effectively by focusing on domain adaptation strategies. The proposed use of standardized simulation-style translation for real-world images to maximize consistency is innovative and has the potential to improve real-world task performance significantly. However, the proposal could benefit from more specifics on evaluation metrics and the anticipated challenges in policy robustness when using only simulated RGB data. Overall, this research has promise for advancing autonomous robotic capabilities.
rating: 8
confidence: 4 |
R5FR5H1fAk | Bridging the Sim-to-Real Gap for Efficient and Robust Robotic Skill Acquisition | [
"Yutao Ouyang",
"Jingzhi Cui"
] | In recent years, the integration of learning-based methods, particularly Imitation Learning (IL) and Reinforcement Learning (RL), has brought significant advancements to robotics applications. These methods enable robots to learn complex behaviors, with IL facilitating rapid skill acquisition via expert demonstrations and RL permitting self-discovery of optimal strategies. However, the reliance on real-world data presents substantial challenges, as data collection can be resource-intensive, time-consuming, and risky for robotic systems. While simulation environments have emerged as a practical solution, providing abundant training data, they also introduce the sim-to-real gap—a critical challenge that hampers the effective transfer of learned behaviors from simulations to real-world scenarios due to discrepancies in sensor performance, environmental conditions, and material properties.
To address these challenges, we propose a novel framework that combines the strengths of both IL and RL while minimizing transfer difficulties associated with simulation-trained models. Our approach leverages the cost-effectiveness of simulated data to enhance robot learning outcomes, utilizing advanced techniques to improve transferability and reduce the sim-to-real gap. By harmonizing the efficiency of IL with the autonomy of RL, we aim to create a more effective learning paradigm that not only accelerates skill acquisition but also enhances real-world applicability. Our results demonstrate that this integrated approach can significantly improve performance in robotic tasks, paving the way for more autonomous and capable robotic systems. | [
"Robotics",
"Sim-to-Real",
"RL",
"IL"
] | https://openreview.net/pdf?id=R5FR5H1fAk | rkV1AbpoSw | official_review | 1,730,881,845,676 | R5FR5H1fAk | [
"everyone"
] | [
"~Shaoting_Zhu1"
] | title: Review of submission 45
review: This proposal presents a novel framework that combines Imitation Learning (IL) and Reinforcement Learning (RL) to address the challenges of transferring robotic skills from simulated environments to real-world scenarios. The authors highlight the sim-to-real gap as a critical issue in robotics, where discrepancies between simulation and real-world conditions can impede the effectiveness of learned behaviors.
**Stength**
1. Innovative Integration of IL and RL: The paper proposes a unique approach that leverages the rapid skill acquisition capabilities of IL and the self-discovery of optimal strategies through RL. This dual approach aims to create a more efficient learning paradigm that enhances both skill acquisition and real-world applicability.
2. Addressing the Sim-to-Real Gap: The authors focus on minimizing the transfer difficulties associated with simulation-trained models, which is a significant contribution to the field of robotics, given the high stakes and risks involved in real-world robotic operations.
**Weakness**
1. Lack of detailed method: The proposed method outlined in this article is rather brief and general, and it does not provide a clear view of the detailed solutions.
2. Tasks and objectives are not clearly defined: The proposal does not specify the type of robot morphology or specific robotic tasks to which the method will be applied, nor does it present clear metrics.
rating: 8
confidence: 4 |
R5FR5H1fAk | Bridging the Sim-to-Real Gap for Efficient and Robust Robotic Skill Acquisition | [
"Yutao Ouyang",
"Jingzhi Cui"
] | In recent years, the integration of learning-based methods, particularly Imitation Learning (IL) and Reinforcement Learning (RL), has brought significant advancements to robotics applications. These methods enable robots to learn complex behaviors, with IL facilitating rapid skill acquisition via expert demonstrations and RL permitting self-discovery of optimal strategies. However, the reliance on real-world data presents substantial challenges, as data collection can be resource-intensive, time-consuming, and risky for robotic systems. While simulation environments have emerged as a practical solution, providing abundant training data, they also introduce the sim-to-real gap—a critical challenge that hampers the effective transfer of learned behaviors from simulations to real-world scenarios due to discrepancies in sensor performance, environmental conditions, and material properties.
To address these challenges, we propose a novel framework that combines the strengths of both IL and RL while minimizing transfer difficulties associated with simulation-trained models. Our approach leverages the cost-effectiveness of simulated data to enhance robot learning outcomes, utilizing advanced techniques to improve transferability and reduce the sim-to-real gap. By harmonizing the efficiency of IL with the autonomy of RL, we aim to create a more effective learning paradigm that not only accelerates skill acquisition but also enhances real-world applicability. Our results demonstrate that this integrated approach can significantly improve performance in robotic tasks, paving the way for more autonomous and capable robotic systems. | [
"Robotics",
"Sim-to-Real",
"RL",
"IL"
] | https://openreview.net/pdf?id=R5FR5H1fAk | ikkUKO5ZuU | official_review | 1,731,034,210,705 | R5FR5H1fAk | [
"everyone"
] | [
"~Lily_Sheng1"
] | title: Submission 45 Review
review: This work explores a method for training robotic systems using a combination of Reinforcement Learning and Imitation Learning to transfer learned policies from simulation to real-world applications. The approach focuses on maximizing transferability by translating real-world images to a consistent simulation style and using RGB-only observations, which are then used to develop a robust grasping policy.
Pros:
1. The exclusive use of simulated data reduces the cost of training with real-world data and real-world data collection.
Cons:
1. Relying completely on simulated RGB data may not be able to capture the entire complexity of real-world environments.
2. There is little detail on how the grasping policy will be tested and applied in a real-world world environment.
rating: 8
confidence: 4 |
R5FR5H1fAk | Bridging the Sim-to-Real Gap for Efficient and Robust Robotic Skill Acquisition | [
"Yutao Ouyang",
"Jingzhi Cui"
] | In recent years, the integration of learning-based methods, particularly Imitation Learning (IL) and Reinforcement Learning (RL), has brought significant advancements to robotics applications. These methods enable robots to learn complex behaviors, with IL facilitating rapid skill acquisition via expert demonstrations and RL permitting self-discovery of optimal strategies. However, the reliance on real-world data presents substantial challenges, as data collection can be resource-intensive, time-consuming, and risky for robotic systems. While simulation environments have emerged as a practical solution, providing abundant training data, they also introduce the sim-to-real gap—a critical challenge that hampers the effective transfer of learned behaviors from simulations to real-world scenarios due to discrepancies in sensor performance, environmental conditions, and material properties.
To address these challenges, we propose a novel framework that combines the strengths of both IL and RL while minimizing transfer difficulties associated with simulation-trained models. Our approach leverages the cost-effectiveness of simulated data to enhance robot learning outcomes, utilizing advanced techniques to improve transferability and reduce the sim-to-real gap. By harmonizing the efficiency of IL with the autonomy of RL, we aim to create a more effective learning paradigm that not only accelerates skill acquisition but also enhances real-world applicability. Our results demonstrate that this integrated approach can significantly improve performance in robotic tasks, paving the way for more autonomous and capable robotic systems. | [
"Robotics",
"Sim-to-Real",
"RL",
"IL"
] | https://openreview.net/pdf?id=R5FR5H1fAk | g3UPypu2f6 | official_review | 1,731,173,436,777 | R5FR5H1fAk | [
"everyone"
] | [
"~Bryan_Constantine_Sadihin1"
] | title: Review for "Bridging the Sim-to-Real Gap for Efficient and Robust Robotic Skill Acquisition"
review: Strength:
1. A really detailed explanation for related research
2. A well-chosen topic, addressing the transfer challenge from simulation to real-world applications is a prospective step toward robotic autonomy.
Weakness:
1. There is no mention of evaluation metrics or baseline models for comparison, which is crucial for assessing research effectiveness.
rating: 9
confidence: 2 |
R5FR5H1fAk | Bridging the Sim-to-Real Gap for Efficient and Robust Robotic Skill Acquisition | [
"Yutao Ouyang",
"Jingzhi Cui"
] | In recent years, the integration of learning-based methods, particularly Imitation Learning (IL) and Reinforcement Learning (RL), has brought significant advancements to robotics applications. These methods enable robots to learn complex behaviors, with IL facilitating rapid skill acquisition via expert demonstrations and RL permitting self-discovery of optimal strategies. However, the reliance on real-world data presents substantial challenges, as data collection can be resource-intensive, time-consuming, and risky for robotic systems. While simulation environments have emerged as a practical solution, providing abundant training data, they also introduce the sim-to-real gap—a critical challenge that hampers the effective transfer of learned behaviors from simulations to real-world scenarios due to discrepancies in sensor performance, environmental conditions, and material properties.
To address these challenges, we propose a novel framework that combines the strengths of both IL and RL while minimizing transfer difficulties associated with simulation-trained models. Our approach leverages the cost-effectiveness of simulated data to enhance robot learning outcomes, utilizing advanced techniques to improve transferability and reduce the sim-to-real gap. By harmonizing the efficiency of IL with the autonomy of RL, we aim to create a more effective learning paradigm that not only accelerates skill acquisition but also enhances real-world applicability. Our results demonstrate that this integrated approach can significantly improve performance in robotic tasks, paving the way for more autonomous and capable robotic systems. | [
"Robotics",
"Sim-to-Real",
"RL",
"IL"
] | https://openreview.net/pdf?id=R5FR5H1fAk | Wapy1cJTrv | official_review | 1,731,169,091,252 | R5FR5H1fAk | [
"everyone"
] | [
"~Lei_Wu17"
] | title: Evaluation of "Bridging the Sim-to-Real Gap for Efficient and Robust Robotic Skill Acquisition"
review: # Pros
* Innovative Approach: The dual-method approach using IL and RL with RGB-only observations could simplify and expedite robot training.
* Cost-Effective: By minimizing real-world data requirements, the framework could lower the cost of training robotic systems.
* Comprehensive Related Work: The paper provides a solid overview of existing methods, framing its approach within current research trends.
* Potential Impact: Addresses a crucial bottleneck in robotic deployment, making it potentially valuable for real-world applications.
# Cons
* Lack of Empirical Evidence: The paper appears to be in a preprint stage and lacks real-world deployment data, leaving claims about real-world performance unverified.
* Limited Discussion on Practical Challenges: While the framework is theoretically sound, more discussion on potential implementation challenges (e.g., variability in real-world lighting and materials) would improve its applicability.
* Narrow Focus on RGB Images: Limiting the approach to RGB images may restrict applicability to specific scenarios or environments where other sensors (e.g., depth, tactile) could enhance robustness.
* Dependency on Simulation Quality: The success of the framework depends heavily on the accuracy of the simulation environment, which may limit generalizability across different robots and tasks.
rating: 7
confidence: 4 |
R5FR5H1fAk | Bridging the Sim-to-Real Gap for Efficient and Robust Robotic Skill Acquisition | [
"Yutao Ouyang",
"Jingzhi Cui"
] | In recent years, the integration of learning-based methods, particularly Imitation Learning (IL) and Reinforcement Learning (RL), has brought significant advancements to robotics applications. These methods enable robots to learn complex behaviors, with IL facilitating rapid skill acquisition via expert demonstrations and RL permitting self-discovery of optimal strategies. However, the reliance on real-world data presents substantial challenges, as data collection can be resource-intensive, time-consuming, and risky for robotic systems. While simulation environments have emerged as a practical solution, providing abundant training data, they also introduce the sim-to-real gap—a critical challenge that hampers the effective transfer of learned behaviors from simulations to real-world scenarios due to discrepancies in sensor performance, environmental conditions, and material properties.
To address these challenges, we propose a novel framework that combines the strengths of both IL and RL while minimizing transfer difficulties associated with simulation-trained models. Our approach leverages the cost-effectiveness of simulated data to enhance robot learning outcomes, utilizing advanced techniques to improve transferability and reduce the sim-to-real gap. By harmonizing the efficiency of IL with the autonomy of RL, we aim to create a more effective learning paradigm that not only accelerates skill acquisition but also enhances real-world applicability. Our results demonstrate that this integrated approach can significantly improve performance in robotic tasks, paving the way for more autonomous and capable robotic systems. | [
"Robotics",
"Sim-to-Real",
"RL",
"IL"
] | https://openreview.net/pdf?id=R5FR5H1fAk | HzdfUM6BFC | official_review | 1,730,883,964,796 | R5FR5H1fAk | [
"everyone"
] | [
"~Chua_Shei_Pern1"
] | title: Good
review: The proposal effectively outlines a hybrid learning approach to bridge the sim-to-real gap in robotic skill acquisition. While the proposed work seems promising, further clarity on specific metrics for evaluating transfer success would enhance the proposal's grounding.
rating: 9
confidence: 4 |
R5FR5H1fAk | Bridging the Sim-to-Real Gap for Efficient and Robust Robotic Skill Acquisition | [
"Yutao Ouyang",
"Jingzhi Cui"
] | In recent years, the integration of learning-based methods, particularly Imitation Learning (IL) and Reinforcement Learning (RL), has brought significant advancements to robotics applications. These methods enable robots to learn complex behaviors, with IL facilitating rapid skill acquisition via expert demonstrations and RL permitting self-discovery of optimal strategies. However, the reliance on real-world data presents substantial challenges, as data collection can be resource-intensive, time-consuming, and risky for robotic systems. While simulation environments have emerged as a practical solution, providing abundant training data, they also introduce the sim-to-real gap—a critical challenge that hampers the effective transfer of learned behaviors from simulations to real-world scenarios due to discrepancies in sensor performance, environmental conditions, and material properties.
To address these challenges, we propose a novel framework that combines the strengths of both IL and RL while minimizing transfer difficulties associated with simulation-trained models. Our approach leverages the cost-effectiveness of simulated data to enhance robot learning outcomes, utilizing advanced techniques to improve transferability and reduce the sim-to-real gap. By harmonizing the efficiency of IL with the autonomy of RL, we aim to create a more effective learning paradigm that not only accelerates skill acquisition but also enhances real-world applicability. Our results demonstrate that this integrated approach can significantly improve performance in robotic tasks, paving the way for more autonomous and capable robotic systems. | [
"Robotics",
"Sim-to-Real",
"RL",
"IL"
] | https://openreview.net/pdf?id=R5FR5H1fAk | 9Ow7kfS1gL | official_review | 1,731,410,153,944 | R5FR5H1fAk | [
"everyone"
] | [
"~Xuancheng_Li1"
] | title: review
review: Summary
This paper proposes a framework that addresses the sim-to-real gap in robotic learning by integrating Imitation Learning (IL) and Reinforcement Learning (RL). The approach leverages simulated environments for efficient data generation while applying techniques to improve the transferability of learned behaviors to real-world conditions. By combining IL's rapid skill acquisition from expert demonstrations with RL's self-discovery capabilities, the framework seeks to enhance both the speed and robustness of robotic skill acquisition.
Strengths
The proposal provides a well-thought-out solution to a major challenge in robotics, demonstrating an innovative combination of IL and RL to balance data efficiency with learning autonomy. The approach holds promise for improving the effectiveness of simulation-based training in real-world applications, potentially accelerating the development of autonomous robotic systems.
Weaknesses
The proposal could benefit from more specifics on the techniques used to bridge the sim-to-real gap, such as domain adaptation or sensor calibration methods. Additionally, an outline of specific metrics for assessing transfer success in real-world tasks would provide clearer insights into the framework's practical effectiveness.
Conclusion
This framework offers a promising advancement in robotic skill acquisition, addressing the practical challenges of simulation-based training. With further refinement, particularly in sim-to-real transfer techniques, this approach could significantly contribute to the field of robotics by enabling more autonomous and adaptable robots.
rating: 9
confidence: 4 |
R5FR5H1fAk | Bridging the Sim-to-Real Gap for Efficient and Robust Robotic Skill Acquisition | [
"Yutao Ouyang",
"Jingzhi Cui"
] | In recent years, the integration of learning-based methods, particularly Imitation Learning (IL) and Reinforcement Learning (RL), has brought significant advancements to robotics applications. These methods enable robots to learn complex behaviors, with IL facilitating rapid skill acquisition via expert demonstrations and RL permitting self-discovery of optimal strategies. However, the reliance on real-world data presents substantial challenges, as data collection can be resource-intensive, time-consuming, and risky for robotic systems. While simulation environments have emerged as a practical solution, providing abundant training data, they also introduce the sim-to-real gap—a critical challenge that hampers the effective transfer of learned behaviors from simulations to real-world scenarios due to discrepancies in sensor performance, environmental conditions, and material properties.
To address these challenges, we propose a novel framework that combines the strengths of both IL and RL while minimizing transfer difficulties associated with simulation-trained models. Our approach leverages the cost-effectiveness of simulated data to enhance robot learning outcomes, utilizing advanced techniques to improve transferability and reduce the sim-to-real gap. By harmonizing the efficiency of IL with the autonomy of RL, we aim to create a more effective learning paradigm that not only accelerates skill acquisition but also enhances real-world applicability. Our results demonstrate that this integrated approach can significantly improve performance in robotic tasks, paving the way for more autonomous and capable robotic systems. | [
"Robotics",
"Sim-to-Real",
"RL",
"IL"
] | https://openreview.net/pdf?id=R5FR5H1fAk | 6dwYU1BLGe | official_review | 1,731,310,304,993 | R5FR5H1fAk | [
"everyone"
] | [
"~Chengming_Shi1"
] | title: Review
review: ### Summary
The proposal "Bridging the Sim-to-Real Gap for Efficient and Robust Robotic Skill Acquisition" aims to develop a novel framework that combines Imitation Learning (IL) and Reinforcement Learning (RL) to enhance the transferability of robotic skills from simulation to real-world environments. The research focuses on addressing the sim-to-real gap by using RGB images and low-cost simulation data to train robots, with the goal of achieving high success rates in real-world tasks without the need for real-world data during training.
### Pros
1. **Combination of IL and RL**: The proposal leverages the strengths of both IL and RL, which could lead to a more efficient and robust learning paradigm for robotic skill acquisition.
2. **Addressing the Sim-to-Real Gap**: The focus on minimizing the sim-to-real gap is crucial for the broader application of simulation-trained models in real-world scenarios.
3. **Cost-Effectiveness**: By using simulation data, the proposed method could significantly reduce the costs associated with real-world data collection, including time, equipment, and potential robot damage.
4. **Innovation in Transferability**: The approach of translating real-world images into a simulation-style format could be a breakthrough in improving the transferability of learned policies.
5. **Direct Deployment**: The aim to develop policies that can be directly deployed in real-world environments without additional real-world training is a significant step towards more autonomous robotic systems.
### Cons
1. **Complexity of Integration**: Combining IL and RL in a way that effectively bridges the sim-to-real gap may be technically challenging and could require complex algorithmic solutions.
2. **Real-World Performance**: There is a risk that even with improved transferability, the performance in real-world scenarios may not meet the high standards set by the proposal.
3. **Data Translation Limitations**: The translation of real-world images into a simulation-style format may not fully capture the complexity and variability of real-world conditions.
4. **Dependence on Simulation Quality**: The success of the proposed method is highly dependent on the quality and realism of the simulation environment, which may not always be achievable.
5. **Scalability**: While the method aims to be cost-effective, there is a question of scalability and whether the approach can be generalized to a wide range of robotic tasks and environments.
rating: 8
confidence: 4 |
QIfYAU6Goy | [Proposal-ML] Enhancing Large Multi-Modal Auto-Regressive Models with Condition Contrastive Alignment | [
"Chendong Xiang",
"Mingdao Liu",
"Yuji Wang"
] | The rapid development of auto-regressive (AR) models in multi-modal generation has brought promising advancements, enabling coherent text, image, and video generation within a single framework. However, AR models still face significant challenges in practical application, especially in image generation where classifier-free guidance (CFG) is commonly used to enhance output quality. CFG, while effective, introduces substantial computational overhead and deviates from the simplicity of end-to-end auto-regressive generation. In this proposal, we aim to explore the potential of Condition Contrastive Alignment (CCA) within Emu3, a state-of-the-art multi-modal AR model, to address the reliance on CFG in image generation. By applying CCA, a recently proposed method for aligning AR models with target distributions through contrastive learning, we hypothesize that Emu3 can achieve comparable or superior output quality without CFG, reducing computational cost and improving generation efficiency. Our approach involves fine-tuning Emu3 with CCA on multi-modal data and conducting comprehensive evaluations across image and video generation benchmarks. This research will validate CCA’s applicability to large AR models, potentially advancing the field towards more efficient, unified multi-modal generation frameworks. | [
"image generation",
"video generation",
"alignment"
] | https://openreview.net/pdf?id=QIfYAU6Goy | qSkQMTD9bq | official_review | 1,731,079,866,173 | QIfYAU6Goy | [
"everyone"
] | [
"~Joydeep_Chandra2"
] | title: Well-structured Proposal for CCA in Multi-Modal AR Models
review: The use of Condition Contrastive Alignment (CCA) to reduce reliance on classifier-free guidance (CFG) is innovative and aims to enhance efficiency. The focus on optimizing multi-modal AR models for image and video generation addresses a current challenge in generative AI. But the proposal needs more specifics on the fine-tuning process and hyperparameter selection. The baseline metrics for comparison with existing CFG-based methods could have been explained well for clarity.
rating: 8
confidence: 4 |
QIfYAU6Goy | [Proposal-ML] Enhancing Large Multi-Modal Auto-Regressive Models with Condition Contrastive Alignment | [
"Chendong Xiang",
"Mingdao Liu",
"Yuji Wang"
] | The rapid development of auto-regressive (AR) models in multi-modal generation has brought promising advancements, enabling coherent text, image, and video generation within a single framework. However, AR models still face significant challenges in practical application, especially in image generation where classifier-free guidance (CFG) is commonly used to enhance output quality. CFG, while effective, introduces substantial computational overhead and deviates from the simplicity of end-to-end auto-regressive generation. In this proposal, we aim to explore the potential of Condition Contrastive Alignment (CCA) within Emu3, a state-of-the-art multi-modal AR model, to address the reliance on CFG in image generation. By applying CCA, a recently proposed method for aligning AR models with target distributions through contrastive learning, we hypothesize that Emu3 can achieve comparable or superior output quality without CFG, reducing computational cost and improving generation efficiency. Our approach involves fine-tuning Emu3 with CCA on multi-modal data and conducting comprehensive evaluations across image and video generation benchmarks. This research will validate CCA’s applicability to large AR models, potentially advancing the field towards more efficient, unified multi-modal generation frameworks. | [
"image generation",
"video generation",
"alignment"
] | https://openreview.net/pdf?id=QIfYAU6Goy | jH9xuKvXIC | official_review | 1,730,972,105,207 | QIfYAU6Goy | [
"everyone"
] | [
"~Huajun_Bai1"
] | title: CCA in Multi-Modal AR Models: Enhancing Efficiency and Addressing Computational Challenges
review: Strengths
1. Innovative Application of CCA: The proposal to integrate Condition Contrastive Alignment (CCA) within Emu3 is a cutting-edge approach that could significantly reduce the reliance on classifier-free guidance (CFG) in image generation, potentially simplifying the process and lowering computational costs.
2. Pushing the Boundaries of Multi-Modal Models: The aim to enhance Emu3, a state-of-the-art multi-modal auto-regressive model, with CCA is commendable. This research could pave the way for more efficient and unified frameworks in multi-modal generation, advancing the field substantially.
3. Rigorous Evaluation Strategy: The proposal includes a thorough plan for fine-tuning Emu3 with CCA and evaluating its performance using established benchmarks and metrics, which is essential for validating the effectiveness of the proposed method.
Risk
1. Data and Compute Intensity: The requirement for a large amount of data and substantial computational resources to train and fine-tune the model presents a significant risk. The proposal does not fully address the potential challenges associated with scaling this approach, which could limit its accessibility and feasibility, especially for research groups with limited resources.
rating: 7
confidence: 3 |
QIfYAU6Goy | [Proposal-ML] Enhancing Large Multi-Modal Auto-Regressive Models with Condition Contrastive Alignment | [
"Chendong Xiang",
"Mingdao Liu",
"Yuji Wang"
] | The rapid development of auto-regressive (AR) models in multi-modal generation has brought promising advancements, enabling coherent text, image, and video generation within a single framework. However, AR models still face significant challenges in practical application, especially in image generation where classifier-free guidance (CFG) is commonly used to enhance output quality. CFG, while effective, introduces substantial computational overhead and deviates from the simplicity of end-to-end auto-regressive generation. In this proposal, we aim to explore the potential of Condition Contrastive Alignment (CCA) within Emu3, a state-of-the-art multi-modal AR model, to address the reliance on CFG in image generation. By applying CCA, a recently proposed method for aligning AR models with target distributions through contrastive learning, we hypothesize that Emu3 can achieve comparable or superior output quality without CFG, reducing computational cost and improving generation efficiency. Our approach involves fine-tuning Emu3 with CCA on multi-modal data and conducting comprehensive evaluations across image and video generation benchmarks. This research will validate CCA’s applicability to large AR models, potentially advancing the field towards more efficient, unified multi-modal generation frameworks. | [
"image generation",
"video generation",
"alignment"
] | https://openreview.net/pdf?id=QIfYAU6Goy | Uw0bbFczVj | official_review | 1,731,327,786,675 | QIfYAU6Goy | [
"everyone"
] | [
"~Hector_Rodriguez_Rodriguez1"
] | title: Review "Enhancing Large Multi-Modal Auto-Regressive Models with Condition Contrastive Alignment"
review: The authors hypothesize that Condition Contrastive Alignment (CCA) can substitute Classifier-Free Guidance (CFG) to improve image generation in a state-of-the-art multimodal AR model such as Emu3. According to the authors, this approach could provide similar or better results at a lower computational cost.
The background provides valuable information on AR multi-modal models and the drawbacks associated with CFG fine-tuning. The proposal provides a formal formulation for the problem and the CCA approach. Finally, the dataset and fine-tuning procedure are disclosed. However, this process could be further detailed to explain the reason for using JourneyDB for fine-tuning and ImageNet for testing.
Overall, the proposal is well-written and meets the submission requirements.
rating: 10
confidence: 3 |
QIfYAU6Goy | [Proposal-ML] Enhancing Large Multi-Modal Auto-Regressive Models with Condition Contrastive Alignment | [
"Chendong Xiang",
"Mingdao Liu",
"Yuji Wang"
] | The rapid development of auto-regressive (AR) models in multi-modal generation has brought promising advancements, enabling coherent text, image, and video generation within a single framework. However, AR models still face significant challenges in practical application, especially in image generation where classifier-free guidance (CFG) is commonly used to enhance output quality. CFG, while effective, introduces substantial computational overhead and deviates from the simplicity of end-to-end auto-regressive generation. In this proposal, we aim to explore the potential of Condition Contrastive Alignment (CCA) within Emu3, a state-of-the-art multi-modal AR model, to address the reliance on CFG in image generation. By applying CCA, a recently proposed method for aligning AR models with target distributions through contrastive learning, we hypothesize that Emu3 can achieve comparable or superior output quality without CFG, reducing computational cost and improving generation efficiency. Our approach involves fine-tuning Emu3 with CCA on multi-modal data and conducting comprehensive evaluations across image and video generation benchmarks. This research will validate CCA’s applicability to large AR models, potentially advancing the field towards more efficient, unified multi-modal generation frameworks. | [
"image generation",
"video generation",
"alignment"
] | https://openreview.net/pdf?id=QIfYAU6Goy | PoYwpORJDS | official_review | 1,731,263,637,730 | QIfYAU6Goy | [
"everyone"
] | [
"~Tong_Yu9"
] | title: Clear proposal
review: Quality:
The paper is well-structured and presents a clear research problem. The authors effectively articulate the limitations of existing methods, particularly the computational burden introduced by CFG. The introduction of CCA is innovative and addresses a significant gap in the current literature on multi-modal generation.
Clarity:
The writing is generally clear, with a logical flow of ideas. The theoretical concepts are explained adequately, and the mathematical formulations are presented in a comprehensible manner. However, some sections could benefit from further elaboration, particularly the practical implications of the CCA method.
Originality:
The introduction of CCA as a means to align auto-regressive models with target distributions through contrastive learning is a noteworthy contribution. This approach is relatively novel in the context of multi-modal generation, and the authors provide a solid justification for its use.
Significance:
The significance of this work lies in its potential to advance the field of multi-modal generation. By reducing the computational overhead associated with CFG, CCA could enable more efficient model training and inference, making high-quality multi-modal generation more accessible.
Pros:
Innovative Approach: The introduction of CCA is a significant advancement in the field.
Comprehensive Evaluation: The authors provide thorough experimental results that validate their claims.
Practical Implications: The reduction in computational overhead is a valuable contribution to the efficiency of multi-modal models.
Cons:
Limited Discussion on Generalizability: The paper could elaborate more on how CCA might perform across different tasks beyond image generation.
Insufficient Detail on Implementation: More details on the implementation of CCA and its integration into existing frameworks would enhance reproducibility.
Potential Overfitting: The risk of overfitting in the fine-tuning process is not addressed, which could impact the generalization of the model.
rating: 8
confidence: 3 |
QIfYAU6Goy | [Proposal-ML] Enhancing Large Multi-Modal Auto-Regressive Models with Condition Contrastive Alignment | [
"Chendong Xiang",
"Mingdao Liu",
"Yuji Wang"
] | The rapid development of auto-regressive (AR) models in multi-modal generation has brought promising advancements, enabling coherent text, image, and video generation within a single framework. However, AR models still face significant challenges in practical application, especially in image generation where classifier-free guidance (CFG) is commonly used to enhance output quality. CFG, while effective, introduces substantial computational overhead and deviates from the simplicity of end-to-end auto-regressive generation. In this proposal, we aim to explore the potential of Condition Contrastive Alignment (CCA) within Emu3, a state-of-the-art multi-modal AR model, to address the reliance on CFG in image generation. By applying CCA, a recently proposed method for aligning AR models with target distributions through contrastive learning, we hypothesize that Emu3 can achieve comparable or superior output quality without CFG, reducing computational cost and improving generation efficiency. Our approach involves fine-tuning Emu3 with CCA on multi-modal data and conducting comprehensive evaluations across image and video generation benchmarks. This research will validate CCA’s applicability to large AR models, potentially advancing the field towards more efficient, unified multi-modal generation frameworks. | [
"image generation",
"video generation",
"alignment"
] | https://openreview.net/pdf?id=QIfYAU6Goy | OAIWHsTzrG | official_review | 1,731,344,113,654 | QIfYAU6Goy | [
"everyone"
] | [
"~Michael_Hua_Wang1"
] | title: Review
review: This proposal describes a well-reasoned mechanism by which the amount of computational resources required for multimodal autoregressive models can be reduced, namely the use of condition contrastive alignment (CCA) to replace classifier-free guidance (CFG). The theory appears to be sound, and there is sufficient detail on how the authors plan to implement their idea.
One cautionary point: the Emu3 paper mentions that CFG was applied to the model to improve image generation quality. I think the results would be more interesting if the CCA-based approach could be applied to a version of the Emu3 prior to the use of CFG to make it easier to contrast the effectiveness of those two approaches.
rating: 10
confidence: 4 |
QIfYAU6Goy | [Proposal-ML] Enhancing Large Multi-Modal Auto-Regressive Models with Condition Contrastive Alignment | [
"Chendong Xiang",
"Mingdao Liu",
"Yuji Wang"
] | The rapid development of auto-regressive (AR) models in multi-modal generation has brought promising advancements, enabling coherent text, image, and video generation within a single framework. However, AR models still face significant challenges in practical application, especially in image generation where classifier-free guidance (CFG) is commonly used to enhance output quality. CFG, while effective, introduces substantial computational overhead and deviates from the simplicity of end-to-end auto-regressive generation. In this proposal, we aim to explore the potential of Condition Contrastive Alignment (CCA) within Emu3, a state-of-the-art multi-modal AR model, to address the reliance on CFG in image generation. By applying CCA, a recently proposed method for aligning AR models with target distributions through contrastive learning, we hypothesize that Emu3 can achieve comparable or superior output quality without CFG, reducing computational cost and improving generation efficiency. Our approach involves fine-tuning Emu3 with CCA on multi-modal data and conducting comprehensive evaluations across image and video generation benchmarks. This research will validate CCA’s applicability to large AR models, potentially advancing the field towards more efficient, unified multi-modal generation frameworks. | [
"image generation",
"video generation",
"alignment"
] | https://openreview.net/pdf?id=QIfYAU6Goy | J6LqhngJsU | official_review | 1,731,291,160,146 | QIfYAU6Goy | [
"everyone"
] | [
"~Matteo_Jiahao_Chen1"
] | title: Good proposal for CCA in Multi-Modal Auto-Regressive Model
review: This proposal introduces the use of Condition Contrastive Alignment (CCA) within Emu3, a state-of-the-art multi-modal auto-regressive (AR) model, as a method to address the reliance on classifier-free guidance (CFG) in image generation. The authors hypothesize that with CCA, they can achieve comparable or superior output quality to CFG while reducing computational costs and improving generation efficiency. .
## Strengths:
1. **Novel Approach:**
The proposal introduces a promising alternative to the commonly used classifier-free guidance (CFG) by leveraging CCA. This method’s use of contrastive learning to align the auto-regressive model’s conditional distribution with the target distribution is innovative, offering potential computational advantages without sacrificing output quality.
2. **Relevance and Impact:**
The research addresses a critical gap in the current multi-modal generative models, specifically the trade-off between output quality and computational efficiency. With the growing demand for more resource-efficient models, this proposal is timely and could have a significant impact on future research in the field of multi-modal generation, especially in scenarios with limited computational resources.
3. **Comprehensive Evaluation Plan:**
The authors plan to conduct thorough evaluations across visual generation benchmarks using well-established metrics (FID and IS).
## Weaknesses:
1. **Insufficient Comparison with Existing Methods:**
. A deeper exploration of how CCA compares to existing techniques in terms of computational cost, output quality, and versatility would be beneficial.
2. **Potential Over-simplification of the Approach:**
While the proposal argues that CCA can reduce computational overhead, it does not fully address potential challenges in model convergence or stability when using this new fine-tuning method.
rating: 9
confidence: 4 |
QIfYAU6Goy | [Proposal-ML] Enhancing Large Multi-Modal Auto-Regressive Models with Condition Contrastive Alignment | [
"Chendong Xiang",
"Mingdao Liu",
"Yuji Wang"
] | The rapid development of auto-regressive (AR) models in multi-modal generation has brought promising advancements, enabling coherent text, image, and video generation within a single framework. However, AR models still face significant challenges in practical application, especially in image generation where classifier-free guidance (CFG) is commonly used to enhance output quality. CFG, while effective, introduces substantial computational overhead and deviates from the simplicity of end-to-end auto-regressive generation. In this proposal, we aim to explore the potential of Condition Contrastive Alignment (CCA) within Emu3, a state-of-the-art multi-modal AR model, to address the reliance on CFG in image generation. By applying CCA, a recently proposed method for aligning AR models with target distributions through contrastive learning, we hypothesize that Emu3 can achieve comparable or superior output quality without CFG, reducing computational cost and improving generation efficiency. Our approach involves fine-tuning Emu3 with CCA on multi-modal data and conducting comprehensive evaluations across image and video generation benchmarks. This research will validate CCA’s applicability to large AR models, potentially advancing the field towards more efficient, unified multi-modal generation frameworks. | [
"image generation",
"video generation",
"alignment"
] | https://openreview.net/pdf?id=QIfYAU6Goy | GqKwcMJbbs | official_review | 1,731,421,053,872 | QIfYAU6Goy | [
"everyone"
] | [
"~Chumeng_Jiang1"
] | title: Detailed Design
review: This proposal aims to enhance large multi-modal auto-regressive (AR) models by integrating Condition Contrastive Alignment (CCA). The approach seeks to reduce the reliance on classifier-free guidance (CFG), which, while effective, is computationally intensive. By fine-tuning Emu3 with CCA on a large multi-modal dataset, the authors aim to achieve high-quality image and video generation while improving computational efficiency. The project will evaluate this approach using benchmarks like Fréchet Inception Distance (FID) and Inception Score (IS).
Strengths:
- **Practical Approach to Reduce Computational Overhead:** The proposal addresses a significant bottleneck in multi-modal AR models by exploring CCA as an alternative to CFG, potentially advancing more efficient AR generation without sacrificing quality.
- **Detailed Design:** The proposed multi-step process of fine-tuning, utilizing CCA with a carefully selected dataset, and thorough evaluation across recognized metrics demonstrates a well-thought-out and robust experimental design.
Weaknesses:
- **Limited Novelty:** It’s unclear how this work significantly differs from existing studies on CCA mentioned in the related work section.
rating: 8
confidence: 4 |
QIfYAU6Goy | [Proposal-ML] Enhancing Large Multi-Modal Auto-Regressive Models with Condition Contrastive Alignment | [
"Chendong Xiang",
"Mingdao Liu",
"Yuji Wang"
] | The rapid development of auto-regressive (AR) models in multi-modal generation has brought promising advancements, enabling coherent text, image, and video generation within a single framework. However, AR models still face significant challenges in practical application, especially in image generation where classifier-free guidance (CFG) is commonly used to enhance output quality. CFG, while effective, introduces substantial computational overhead and deviates from the simplicity of end-to-end auto-regressive generation. In this proposal, we aim to explore the potential of Condition Contrastive Alignment (CCA) within Emu3, a state-of-the-art multi-modal AR model, to address the reliance on CFG in image generation. By applying CCA, a recently proposed method for aligning AR models with target distributions through contrastive learning, we hypothesize that Emu3 can achieve comparable or superior output quality without CFG, reducing computational cost and improving generation efficiency. Our approach involves fine-tuning Emu3 with CCA on multi-modal data and conducting comprehensive evaluations across image and video generation benchmarks. This research will validate CCA’s applicability to large AR models, potentially advancing the field towards more efficient, unified multi-modal generation frameworks. | [
"image generation",
"video generation",
"alignment"
] | https://openreview.net/pdf?id=QIfYAU6Goy | 9dWRdxq0RH | official_review | 1,731,307,771,218 | QIfYAU6Goy | [
"everyone"
] | [
"~Rim_El_Filali1"
] | title: Well-written Research Proposal for Enhancing Multi-Modal AR Models Efficiency with CCA
review: This proposal introduces a good approach for improving efficiency in multi-modal auto-regressive (AR) models by integrating CCA into Emu3, a state-of-the-art AR model for text, image, and video generation. The proposed use of CCA seeks to reduce reliance on CFG for image generation, which traditionally increases computational costs.
Pros:
- The proposal clearly defines the mathematical foundation of CCA, including loss function formulations, which strengthens the theoretical foundation.
- The use of multiple metrics and datasets provides a well-rounded approach to assessing the quality and efficiency of CCA integration.
Cons:
- Further explanation of hyper-parameter selection and how they impact the training process would enhance understanding and reproducibility.
- There is no discussion on potential limitations where CCA might not reduce computational load as expected or might degrade quality.
rating: 8
confidence: 4 |
QIfYAU6Goy | [Proposal-ML] Enhancing Large Multi-Modal Auto-Regressive Models with Condition Contrastive Alignment | [
"Chendong Xiang",
"Mingdao Liu",
"Yuji Wang"
] | The rapid development of auto-regressive (AR) models in multi-modal generation has brought promising advancements, enabling coherent text, image, and video generation within a single framework. However, AR models still face significant challenges in practical application, especially in image generation where classifier-free guidance (CFG) is commonly used to enhance output quality. CFG, while effective, introduces substantial computational overhead and deviates from the simplicity of end-to-end auto-regressive generation. In this proposal, we aim to explore the potential of Condition Contrastive Alignment (CCA) within Emu3, a state-of-the-art multi-modal AR model, to address the reliance on CFG in image generation. By applying CCA, a recently proposed method for aligning AR models with target distributions through contrastive learning, we hypothesize that Emu3 can achieve comparable or superior output quality without CFG, reducing computational cost and improving generation efficiency. Our approach involves fine-tuning Emu3 with CCA on multi-modal data and conducting comprehensive evaluations across image and video generation benchmarks. This research will validate CCA’s applicability to large AR models, potentially advancing the field towards more efficient, unified multi-modal generation frameworks. | [
"image generation",
"video generation",
"alignment"
] | https://openreview.net/pdf?id=QIfYAU6Goy | 9KrpZCclpK | official_review | 1,730,884,118,396 | QIfYAU6Goy | [
"everyone"
] | [
"~Aleksandr_Algazinov1"
] | title: Clear motivation and attention to details
review: The proposal is well-written and easy to read. The authors consider the problem of multimodal content generation, and suggest using autoregressive models. Based on various references, the authors propose to use a specific fine tuning method (CCA) on the Emu3 model. The authors explain in detail the method, motivation, and potential effect of the study.
rating: 10
confidence: 4 |
QIfYAU6Goy | [Proposal-ML] Enhancing Large Multi-Modal Auto-Regressive Models with Condition Contrastive Alignment | [
"Chendong Xiang",
"Mingdao Liu",
"Yuji Wang"
] | The rapid development of auto-regressive (AR) models in multi-modal generation has brought promising advancements, enabling coherent text, image, and video generation within a single framework. However, AR models still face significant challenges in practical application, especially in image generation where classifier-free guidance (CFG) is commonly used to enhance output quality. CFG, while effective, introduces substantial computational overhead and deviates from the simplicity of end-to-end auto-regressive generation. In this proposal, we aim to explore the potential of Condition Contrastive Alignment (CCA) within Emu3, a state-of-the-art multi-modal AR model, to address the reliance on CFG in image generation. By applying CCA, a recently proposed method for aligning AR models with target distributions through contrastive learning, we hypothesize that Emu3 can achieve comparable or superior output quality without CFG, reducing computational cost and improving generation efficiency. Our approach involves fine-tuning Emu3 with CCA on multi-modal data and conducting comprehensive evaluations across image and video generation benchmarks. This research will validate CCA’s applicability to large AR models, potentially advancing the field towards more efficient, unified multi-modal generation frameworks. | [
"image generation",
"video generation",
"alignment"
] | https://openreview.net/pdf?id=QIfYAU6Goy | 9HVSp3C5YG | official_review | 1,731,343,445,262 | QIfYAU6Goy | [
"everyone"
] | [
"~Gausse_Mael_DONGMO_KENFACK1"
] | title: Well formulated and interesting
review: The paper explores improving multi-modal auto-regressive (AR) models, specifically the Emu3 model, by reducing reliance on classifier-free guidance (CFG) during image generation. The authors propose leveraging Condition Contrastive Alignment (CCA), which they hypothesize can produce high-quality outputs with reduced computational cost.
strength: The methodology is well-defined, especially the use of CCA as an alternative to CFG, and details on adapting it to Emu3 are outlined effectively.
weakness: A direct comparison with other AR models that utilize CFG would enhance the argument for CCA computational efficiency and quality gains.
rating: 8
confidence: 3 |
QIfYAU6Goy | [Proposal-ML] Enhancing Large Multi-Modal Auto-Regressive Models with Condition Contrastive Alignment | [
"Chendong Xiang",
"Mingdao Liu",
"Yuji Wang"
] | The rapid development of auto-regressive (AR) models in multi-modal generation has brought promising advancements, enabling coherent text, image, and video generation within a single framework. However, AR models still face significant challenges in practical application, especially in image generation where classifier-free guidance (CFG) is commonly used to enhance output quality. CFG, while effective, introduces substantial computational overhead and deviates from the simplicity of end-to-end auto-regressive generation. In this proposal, we aim to explore the potential of Condition Contrastive Alignment (CCA) within Emu3, a state-of-the-art multi-modal AR model, to address the reliance on CFG in image generation. By applying CCA, a recently proposed method for aligning AR models with target distributions through contrastive learning, we hypothesize that Emu3 can achieve comparable or superior output quality without CFG, reducing computational cost and improving generation efficiency. Our approach involves fine-tuning Emu3 with CCA on multi-modal data and conducting comprehensive evaluations across image and video generation benchmarks. This research will validate CCA’s applicability to large AR models, potentially advancing the field towards more efficient, unified multi-modal generation frameworks. | [
"image generation",
"video generation",
"alignment"
] | https://openreview.net/pdf?id=QIfYAU6Goy | 6Lvxq8Z69q | official_review | 1,731,403,360,364 | QIfYAU6Goy | [
"everyone"
] | [
"~Grace_Xin-Yue_Yi1"
] | title: Review
review: The proposal provides a comprehensive background, explaining the advancements and limitations of multi-modal AR models, particularly in image generation where classifier-free guidance (CFG) introduces computational inefficiencies. The proposal thoroughly covers related work on AR models, CFG, and CCA, presenting recent advancements in multi-modal AR generation. It follows up with a clear problem formulation and a detailed proposed methodology.
rating: 10
confidence: 3 |
MlIDiLsAwq | 【Proposal】RBPA: Retrieval-Augmented-Generation based personal investment assistant | [
"Xue Zeng",
"Yinuo Li"
] | Large language models (LLMs) have made significant strides in recent years, with their capabilities being harnessed across a multitude of industries for various applications. These models, with their vast parameter counts, are designed to handle complex tasks and data, offering enhanced expressiveness and predictive performance.
Although LLMs can grasp basic world knowledge, they cannot be directly applied to financial markets in dynamic games. The influencing factors of the financial market are complex, from macro to micro and involve a wide range of aspects, and the analysis of the financial market needs to establish a professional knowledge base in the financial field, including basic professional knowledge, logical chain knowledge, and related network knowledge.
Retrieval-Augmented Generation (RAG) is a groundbreaking approach that marries the capabilities of large language models with the precision of information retrieval from reliable database. By leveraging a vast knowledge base, RAG enables the generation of highly accurate, relevant, and timely responses, making it an ideal technology for a personal investment assistant, under which circumstances lots of professional data and personal data are required. | [
"RAG; Investment Assitant; Personal data"
] | https://openreview.net/pdf?id=MlIDiLsAwq | svKen43Zpl | official_review | 1,731,330,663,899 | MlIDiLsAwq | [
"everyone"
] | [
"~Yang_Ouyang2"
] | title: Innovative and Clear but lack conciseness in technical details
review: Strengths
Relevant: The proposal addresses an important gap in the financial sector of providing personalized, data-driven investment insights.
Clear Methodology: The outlined approach is clear.
Innovative Use of RAG: Using RAG and a graph database to preserve complex relationships between data points is a refreshing take
Weaknesses
Vague Details: The proposal lacks implementation details of the graph database and LLM fine-tuning
Lacks Privacy Considerations: Countries have data privacy and compliance laws.
Absence of evaluation plan.
Overall, the proposal presents an innovative approach to enhancing financial market analysis using RAG and LLMs but would benefit from clearer technical details, data privacy considerations, and an evaluation plan.
rating: 9
confidence: 4 |
MlIDiLsAwq | 【Proposal】RBPA: Retrieval-Augmented-Generation based personal investment assistant | [
"Xue Zeng",
"Yinuo Li"
] | Large language models (LLMs) have made significant strides in recent years, with their capabilities being harnessed across a multitude of industries for various applications. These models, with their vast parameter counts, are designed to handle complex tasks and data, offering enhanced expressiveness and predictive performance.
Although LLMs can grasp basic world knowledge, they cannot be directly applied to financial markets in dynamic games. The influencing factors of the financial market are complex, from macro to micro and involve a wide range of aspects, and the analysis of the financial market needs to establish a professional knowledge base in the financial field, including basic professional knowledge, logical chain knowledge, and related network knowledge.
Retrieval-Augmented Generation (RAG) is a groundbreaking approach that marries the capabilities of large language models with the precision of information retrieval from reliable database. By leveraging a vast knowledge base, RAG enables the generation of highly accurate, relevant, and timely responses, making it an ideal technology for a personal investment assistant, under which circumstances lots of professional data and personal data are required. | [
"RAG; Investment Assitant; Personal data"
] | https://openreview.net/pdf?id=MlIDiLsAwq | junj5Uk2Fg | official_review | 1,731,262,855,270 | MlIDiLsAwq | [
"everyone"
] | [
"~Tong_Yu9"
] | title: Review of "Proposal of RBPA: Retrieval-Augmented-Generation based Personal Investment Assistant"
review: Quality
The quality of the work is commendable, as it combines established techniques in natural language processing with innovative applications in finance. The authors provide a clear framework for the proposed RBPA system, detailing the need for both external databases and enhanced logical capabilities in LLMs.
Clarity
The paper is generally well-structured and easy to follow. The introduction effectively outlines the problem and the significance of the proposed solution. However, some sections could benefit from more detailed explanations, particularly regarding the implementation of the graph database and the methods for enhancing LLMs' analytical capabilities.
Originality
The approach of combining RAG with LLMs for personalized investment advice is original and timely. While there have been previous works on LLMs in finance, the specific integration of RAG to address the complexities of personal investment is a novel contribution to the field.
Significance
This work holds significant potential for advancing the use of AI in financial decision-making. By addressing the limitations of current systems and proposing a method that combines professional and personal data, the RBPA system could enhance the accessibility and effectiveness of investment advice for individual investors.
Pros
Innovative Approach: The integration of RAG with LLMs for investment advice is a fresh perspective in financial technology.
Addressing Real Needs: The system aims to fill a gap in personalized investment recommendations, which is crucial for individual investors.
Strong Theoretical Foundation: The paper builds on existing research in RAG and LLMs, providing a solid theoretical basis for the proposed system.
Cons
Implementation Details: The paper lacks in-depth discussion on the practical implementation of the graph database and the methods for enhancing logical reasoning in LLMs.
Experimental Validation: There is insufficient information on experimental results or case studies that validate the effectiveness of the proposed system.
Data Collection Concerns: The methods for ensuring the accuracy and timeliness of the data collected for the external database are not clearly outlined.
rating: 8
confidence: 4 |
MlIDiLsAwq | 【Proposal】RBPA: Retrieval-Augmented-Generation based personal investment assistant | [
"Xue Zeng",
"Yinuo Li"
] | Large language models (LLMs) have made significant strides in recent years, with their capabilities being harnessed across a multitude of industries for various applications. These models, with their vast parameter counts, are designed to handle complex tasks and data, offering enhanced expressiveness and predictive performance.
Although LLMs can grasp basic world knowledge, they cannot be directly applied to financial markets in dynamic games. The influencing factors of the financial market are complex, from macro to micro and involve a wide range of aspects, and the analysis of the financial market needs to establish a professional knowledge base in the financial field, including basic professional knowledge, logical chain knowledge, and related network knowledge.
Retrieval-Augmented Generation (RAG) is a groundbreaking approach that marries the capabilities of large language models with the precision of information retrieval from reliable database. By leveraging a vast knowledge base, RAG enables the generation of highly accurate, relevant, and timely responses, making it an ideal technology for a personal investment assistant, under which circumstances lots of professional data and personal data are required. | [
"RAG; Investment Assitant; Personal data"
] | https://openreview.net/pdf?id=MlIDiLsAwq | gYyubGearO | official_review | 1,731,141,888,034 | MlIDiLsAwq | [
"everyone"
] | [
"~Ziang_Zheng1"
] | title: an innovative approach to improving large language model (LLM) performance in financial markets through a Retrieval-Augmented Generation (RAG)-based system
review: The paper presents an innovative approach to improving large language model (LLM) performance in financial markets through a Retrieval-Augmented Generation (RAG)-based system, named RBPA. This system aims to generate personalized and data-informed investment advice by combining graph databases with LLMs. The methodology is well-conceived, and the proposal is timely given the growing intersection of AI and finance. However, there are several areas where the paper could benefit from refinement and additional clarity.
### Strengths
1. **Relevant Problem Statement**: The paper clearly identifies the complexity of financial markets as an application domain for LLMs. The focus on creating a personalized investment assistant via RAG is compelling and addresses a real-world need.
2. **Novel Approach Using Graph Databases**: Introducing a graph database to retain long-distance relationships between data points is a notable innovation. This addresses the limitation of vector databases that often struggle to capture context across widely separated text segments.
3. **Flexible Strategy for Model Training**: The paper’s dual approach to enhancing the model’s analytical abilities—either using external high-quality analyses or fine-tuning the LLM if needed—demonstrates an adaptable methodology that aligns well with model performance requirements.
### Areas for Improvement
1. **Real-Time Data Management**: The financial market is highly dynamic, with constantly changing data. A more detailed explanation of how the graph database will be updated and maintained in real-time would greatly improve the proposal. Readers would benefit from a discussion on the methods and technologies for ensuring timely, accurate data updates.
2. **Data Quality and Bias**: The system’s reliance on varied data sources such as news articles, financial reports, and regulations raises potential concerns about data consistency and quality. Clarifying how the model will handle and mitigate biases in these sources could enhance the paper’s rigor, especially given the impact of biased data on investment recommendations.
3. **Comparative Analysis with Existing Systems**: Although the paper mentions recent advancements in RAG systems and industry applications (e.g., Citadel’s use of ChatGPT), a more thorough comparison between RBPA and similar existing systems would strengthen the contribution. Highlighting specific challenges unique to the financial industry and how RBPA addresses them would provide a clearer value proposition.
4. **Scalability and Performance Evaluation**: Given the complexity of RBPA’s data pipeline, details on the system’s scalability and potential bottlenecks would be valuable. Additionally, the authors could consider including preliminary benchmarks or plans for evaluating the system’s performance, both in terms of retrieval speed and recommendation quality.
5. **Data Privacy and Personalization**: Since the system integrates personal data with professional financial information, a discussion on data privacy considerations is essential. This includes methods for securing personal information, especially if the system is deployed in real-world settings where regulatory compliance may be required.
### Recommendation
The paper has potential as it addresses a significant problem with a novel approach. Addressing the above concerns will make it more robust and applicable to real-world financial applications. I recommend acceptance with major revisions, especially focusing on real-time data management, bias handling, and performance evaluation. Expanding on these areas will help make RBPA a stronger and more competitive solution in the AI-driven finance landscape.
**Suggested Action**: Accept with Major Revisions
rating: 7
confidence: 3 |
MlIDiLsAwq | 【Proposal】RBPA: Retrieval-Augmented-Generation based personal investment assistant | [
"Xue Zeng",
"Yinuo Li"
] | Large language models (LLMs) have made significant strides in recent years, with their capabilities being harnessed across a multitude of industries for various applications. These models, with their vast parameter counts, are designed to handle complex tasks and data, offering enhanced expressiveness and predictive performance.
Although LLMs can grasp basic world knowledge, they cannot be directly applied to financial markets in dynamic games. The influencing factors of the financial market are complex, from macro to micro and involve a wide range of aspects, and the analysis of the financial market needs to establish a professional knowledge base in the financial field, including basic professional knowledge, logical chain knowledge, and related network knowledge.
Retrieval-Augmented Generation (RAG) is a groundbreaking approach that marries the capabilities of large language models with the precision of information retrieval from reliable database. By leveraging a vast knowledge base, RAG enables the generation of highly accurate, relevant, and timely responses, making it an ideal technology for a personal investment assistant, under which circumstances lots of professional data and personal data are required. | [
"RAG; Investment Assitant; Personal data"
] | https://openreview.net/pdf?id=MlIDiLsAwq | dedYF3QDfG | official_review | 1,731,413,283,824 | MlIDiLsAwq | [
"everyone"
] | [
"~Gangxin_Xu1"
] | title: Review of "RBPA: Retrieval-Augmented-Generation-Based Personal Investment Assistant"
review: The RBPA proposal creatively combines a RAG model with a graph database to enhance financial data retrieval, offering potential advancements over traditional systems. The approach is well-motivated, addressing the need for personalized investment support by capturing complex data relationships. However, the proposal would benefit from more technical details and a clearer evaluation plan.
Strengths:
Innovative Database Use: Leveraging a graph database for contextual data retrieval is a strong enhancement over vector-based systems.
Relevant Problem and Adaptable Approach: The proposal clearly identifies the complexity of personalized investment advice and adapts to performance needs through flexible data handling.
rating: 9
confidence: 5 |
MlIDiLsAwq | 【Proposal】RBPA: Retrieval-Augmented-Generation based personal investment assistant | [
"Xue Zeng",
"Yinuo Li"
] | Large language models (LLMs) have made significant strides in recent years, with their capabilities being harnessed across a multitude of industries for various applications. These models, with their vast parameter counts, are designed to handle complex tasks and data, offering enhanced expressiveness and predictive performance.
Although LLMs can grasp basic world knowledge, they cannot be directly applied to financial markets in dynamic games. The influencing factors of the financial market are complex, from macro to micro and involve a wide range of aspects, and the analysis of the financial market needs to establish a professional knowledge base in the financial field, including basic professional knowledge, logical chain knowledge, and related network knowledge.
Retrieval-Augmented Generation (RAG) is a groundbreaking approach that marries the capabilities of large language models with the precision of information retrieval from reliable database. By leveraging a vast knowledge base, RAG enables the generation of highly accurate, relevant, and timely responses, making it an ideal technology for a personal investment assistant, under which circumstances lots of professional data and personal data are required. | [
"RAG; Investment Assitant; Personal data"
] | https://openreview.net/pdf?id=MlIDiLsAwq | ZMQQs0e9WN | official_review | 1,731,345,287,548 | MlIDiLsAwq | [
"everyone"
] | [
"~Liu_Yiyang1"
] | title: Innovative idea that could use more details
review: Generally, the problem's background is well established, and the prospect of using RBPA is promising. Using a graph database to capture long-range relationships between financial data points marks a departure from traditional vector-based retrieval, potentially enhancing the depth and relevance of retrieved information. However, when it came to the methodology, a lot of details were vague. For example, how exactly will the graph database capture complex financial relationships, organize data efficiently, and support fast retrieval? What are the "certain ways" mentioned used to classify information in the last sentence of section 2.1? Evaluation metrics might need a little more details as well, as that will be pivotal to discern when to switch methodologies as mentioned in the first paragraph of section 2.2.
rating: 7
confidence: 3 |
MlIDiLsAwq | 【Proposal】RBPA: Retrieval-Augmented-Generation based personal investment assistant | [
"Xue Zeng",
"Yinuo Li"
] | Large language models (LLMs) have made significant strides in recent years, with their capabilities being harnessed across a multitude of industries for various applications. These models, with their vast parameter counts, are designed to handle complex tasks and data, offering enhanced expressiveness and predictive performance.
Although LLMs can grasp basic world knowledge, they cannot be directly applied to financial markets in dynamic games. The influencing factors of the financial market are complex, from macro to micro and involve a wide range of aspects, and the analysis of the financial market needs to establish a professional knowledge base in the financial field, including basic professional knowledge, logical chain knowledge, and related network knowledge.
Retrieval-Augmented Generation (RAG) is a groundbreaking approach that marries the capabilities of large language models with the precision of information retrieval from reliable database. By leveraging a vast knowledge base, RAG enables the generation of highly accurate, relevant, and timely responses, making it an ideal technology for a personal investment assistant, under which circumstances lots of professional data and personal data are required. | [
"RAG; Investment Assitant; Personal data"
] | https://openreview.net/pdf?id=MlIDiLsAwq | YMNaVPD4N2 | official_review | 1,731,313,527,896 | MlIDiLsAwq | [
"everyone"
] | [
"~Wenjing_Wu1"
] | title: Review
review: **Summary**:
The proposal introduces a Retrieval-Augmented Generation (RAG)-based method to support personal investment decision-making. A notable innovation in this approach is the construction of a graph database tailored to the investment domain, enhancing data organization and contextual understanding.
**Strengths**:
- Well-Structured: The proposal is organized clearly which makes it easy to understand.
- Adequate Background Research: It provides sufficient background information, establishing a strong foundation for the proposed method.
- Innovative Idea: The use of a graph database specifically designed for personal investment is a creative and promising concept
**Weaknesses**:
- Lack of Details in Graph Database Construction: The proposal lacks specifics on how the graph database will be constructed
- Unclear Performance Evaluation Metrics: It’s unclear how the performance of the proposed method will be evaluated. Outlining measurable evaluation criteria would strengthen the proposal by providing a way to assess its effectiveness.
rating: 7
confidence: 3 |
MlIDiLsAwq | 【Proposal】RBPA: Retrieval-Augmented-Generation based personal investment assistant | [
"Xue Zeng",
"Yinuo Li"
] | Large language models (LLMs) have made significant strides in recent years, with their capabilities being harnessed across a multitude of industries for various applications. These models, with their vast parameter counts, are designed to handle complex tasks and data, offering enhanced expressiveness and predictive performance.
Although LLMs can grasp basic world knowledge, they cannot be directly applied to financial markets in dynamic games. The influencing factors of the financial market are complex, from macro to micro and involve a wide range of aspects, and the analysis of the financial market needs to establish a professional knowledge base in the financial field, including basic professional knowledge, logical chain knowledge, and related network knowledge.
Retrieval-Augmented Generation (RAG) is a groundbreaking approach that marries the capabilities of large language models with the precision of information retrieval from reliable database. By leveraging a vast knowledge base, RAG enables the generation of highly accurate, relevant, and timely responses, making it an ideal technology for a personal investment assistant, under which circumstances lots of professional data and personal data are required. | [
"RAG; Investment Assitant; Personal data"
] | https://openreview.net/pdf?id=MlIDiLsAwq | RufM5bhmt7 | official_review | 1,731,336,407,560 | MlIDiLsAwq | [
"everyone"
] | [
"~Jiaxiang_Liu7"
] | title: Promising Proposal with Novel Approach
review: This proposal outlines the development of a personal investment assistant, RBPA, leveraging Retrieval-Augmented Generation (RAG) to combine large language model capabilities with real-time and domain-specific data retrieval. By utilizing a graph database for capturing complex relationships within financial data and enhancing the model’s logical analysis in financial contexts, RBPA aims to provide more accurate and personalized investment insights. The approach is well-motivated and technically sound, though it would benefit from clearer implementation details and metrics for assessing performance. Overall, this proposal is promising and has the potential to bridge important gaps in personalized financial advice systems.
rating: 7
confidence: 4 |
MlIDiLsAwq | 【Proposal】RBPA: Retrieval-Augmented-Generation based personal investment assistant | [
"Xue Zeng",
"Yinuo Li"
] | Large language models (LLMs) have made significant strides in recent years, with their capabilities being harnessed across a multitude of industries for various applications. These models, with their vast parameter counts, are designed to handle complex tasks and data, offering enhanced expressiveness and predictive performance.
Although LLMs can grasp basic world knowledge, they cannot be directly applied to financial markets in dynamic games. The influencing factors of the financial market are complex, from macro to micro and involve a wide range of aspects, and the analysis of the financial market needs to establish a professional knowledge base in the financial field, including basic professional knowledge, logical chain knowledge, and related network knowledge.
Retrieval-Augmented Generation (RAG) is a groundbreaking approach that marries the capabilities of large language models with the precision of information retrieval from reliable database. By leveraging a vast knowledge base, RAG enables the generation of highly accurate, relevant, and timely responses, making it an ideal technology for a personal investment assistant, under which circumstances lots of professional data and personal data are required. | [
"RAG; Investment Assitant; Personal data"
] | https://openreview.net/pdf?id=MlIDiLsAwq | P4zLRyv43m | official_review | 1,731,040,732,063 | MlIDiLsAwq | [
"everyone"
] | [
"~Ethan_Wei_Yuxin1"
] | title: The good and the bad of this paper
review: It is great that this paper considers using varied data sources and alternate data sources like price data, news, laws and regulation on top of financial and analyst reports. RAG is a great way to ensure high reliability from a low likelihood of hallucinations of an LLM. However, I feel that the technicalities on how the paper plans to introduce "logical support" could be further expanded upon. If a RAG system is in use, I'm not too sure if fine-tuning should be part of the process as well, since that may defeat the purpose of a RAG system which allows for vector database retrieval without needing to train an LLM on the new data.
rating: 7
confidence: 4 |
MlIDiLsAwq | 【Proposal】RBPA: Retrieval-Augmented-Generation based personal investment assistant | [
"Xue Zeng",
"Yinuo Li"
] | Large language models (LLMs) have made significant strides in recent years, with their capabilities being harnessed across a multitude of industries for various applications. These models, with their vast parameter counts, are designed to handle complex tasks and data, offering enhanced expressiveness and predictive performance.
Although LLMs can grasp basic world knowledge, they cannot be directly applied to financial markets in dynamic games. The influencing factors of the financial market are complex, from macro to micro and involve a wide range of aspects, and the analysis of the financial market needs to establish a professional knowledge base in the financial field, including basic professional knowledge, logical chain knowledge, and related network knowledge.
Retrieval-Augmented Generation (RAG) is a groundbreaking approach that marries the capabilities of large language models with the precision of information retrieval from reliable database. By leveraging a vast knowledge base, RAG enables the generation of highly accurate, relevant, and timely responses, making it an ideal technology for a personal investment assistant, under which circumstances lots of professional data and personal data are required. | [
"RAG; Investment Assitant; Personal data"
] | https://openreview.net/pdf?id=MlIDiLsAwq | P1UCOcuf8z | official_review | 1,731,302,054,302 | MlIDiLsAwq | [
"everyone"
] | [
"~Guanglei_He1"
] | title: Good proposal, but it lacks depth.
review: The proposal introduces an idea but does not provide a clear approach for validating it. I understand that the core issue here lies in establishing a benchmark dataset. Without a way to quantify this problem, it will be challenging to achieve a meaningful solution.
The key concept of how to use RAG is not clearly explained, especially regarding the current limitations and technical challenges of RAG. Do these technical shortcomings make it very difficult to achieve the proposal’s objectives?
Overall, the issue is that the proposal is overly generic and lacks depth. Wouldn’t applying this concept to personal assistants or knowledge-based Q&A yield similar results?
rating: 8
confidence: 3 |
MlIDiLsAwq | 【Proposal】RBPA: Retrieval-Augmented-Generation based personal investment assistant | [
"Xue Zeng",
"Yinuo Li"
] | Large language models (LLMs) have made significant strides in recent years, with their capabilities being harnessed across a multitude of industries for various applications. These models, with their vast parameter counts, are designed to handle complex tasks and data, offering enhanced expressiveness and predictive performance.
Although LLMs can grasp basic world knowledge, they cannot be directly applied to financial markets in dynamic games. The influencing factors of the financial market are complex, from macro to micro and involve a wide range of aspects, and the analysis of the financial market needs to establish a professional knowledge base in the financial field, including basic professional knowledge, logical chain knowledge, and related network knowledge.
Retrieval-Augmented Generation (RAG) is a groundbreaking approach that marries the capabilities of large language models with the precision of information retrieval from reliable database. By leveraging a vast knowledge base, RAG enables the generation of highly accurate, relevant, and timely responses, making it an ideal technology for a personal investment assistant, under which circumstances lots of professional data and personal data are required. | [
"RAG; Investment Assitant; Personal data"
] | https://openreview.net/pdf?id=MlIDiLsAwq | IToGoj53U1 | official_review | 1,731,049,256,529 | MlIDiLsAwq | [
"everyone"
] | [
"~Peidong_Zhang1"
] | title: Strengths and limitations of proposal
review: This proposal presents RBPA, a RAG-based system for personalized investment advice, leveraging external financial data and enhancing LLM capabilities through a graph database and fine-tuning. The approach of using a graph database for richer information retrieval is a key strength, offering the potential for more nuanced financial recommendations. However, the proposal lacks details on performance evaluation, data privacy concerns, and the scalability of the system in dynamic real-world scenarios. These gaps need to be addressed to strengthen the feasibility and impact of the proposed system.
rating: 7
confidence: 4 |
MlIDiLsAwq | 【Proposal】RBPA: Retrieval-Augmented-Generation based personal investment assistant | [
"Xue Zeng",
"Yinuo Li"
] | Large language models (LLMs) have made significant strides in recent years, with their capabilities being harnessed across a multitude of industries for various applications. These models, with their vast parameter counts, are designed to handle complex tasks and data, offering enhanced expressiveness and predictive performance.
Although LLMs can grasp basic world knowledge, they cannot be directly applied to financial markets in dynamic games. The influencing factors of the financial market are complex, from macro to micro and involve a wide range of aspects, and the analysis of the financial market needs to establish a professional knowledge base in the financial field, including basic professional knowledge, logical chain knowledge, and related network knowledge.
Retrieval-Augmented Generation (RAG) is a groundbreaking approach that marries the capabilities of large language models with the precision of information retrieval from reliable database. By leveraging a vast knowledge base, RAG enables the generation of highly accurate, relevant, and timely responses, making it an ideal technology for a personal investment assistant, under which circumstances lots of professional data and personal data are required. | [
"RAG; Investment Assitant; Personal data"
] | https://openreview.net/pdf?id=MlIDiLsAwq | 7TtwmPc3Cs | official_review | 1,731,423,202,064 | MlIDiLsAwq | [
"everyone"
] | [
"~liyingxin1"
] | title: Should add more detail about the background to illustrate why this task is suitable.
review: Investment is a very interesting but random field. It is a special task, so maybe it is very good to use this method and maybe not. The reason to choose it should be clarified. Also, it is suggested to elaborate on the sources and methods of data collection, especially in terms of acquiring and processing financial data, to ensure the reliability and timeliness of the data.
rating: 7
confidence: 3 |
MlIDiLsAwq | 【Proposal】RBPA: Retrieval-Augmented-Generation based personal investment assistant | [
"Xue Zeng",
"Yinuo Li"
] | Large language models (LLMs) have made significant strides in recent years, with their capabilities being harnessed across a multitude of industries for various applications. These models, with their vast parameter counts, are designed to handle complex tasks and data, offering enhanced expressiveness and predictive performance.
Although LLMs can grasp basic world knowledge, they cannot be directly applied to financial markets in dynamic games. The influencing factors of the financial market are complex, from macro to micro and involve a wide range of aspects, and the analysis of the financial market needs to establish a professional knowledge base in the financial field, including basic professional knowledge, logical chain knowledge, and related network knowledge.
Retrieval-Augmented Generation (RAG) is a groundbreaking approach that marries the capabilities of large language models with the precision of information retrieval from reliable database. By leveraging a vast knowledge base, RAG enables the generation of highly accurate, relevant, and timely responses, making it an ideal technology for a personal investment assistant, under which circumstances lots of professional data and personal data are required. | [
"RAG; Investment Assitant; Personal data"
] | https://openreview.net/pdf?id=MlIDiLsAwq | 6LqQ9RJYkJ | official_review | 1,731,227,478,121 | MlIDiLsAwq | [
"everyone"
] | [
"~Chan_Thong_Fong1"
] | title: Evaluating the Innovation and Empirical Rigor of RBPA’s Graph-Based Financial Advisory System
review: It is very innovative to use a graph database to enhance RBPA’s ability to capture complex relationships within financial data, thus increasing the model’s precision and relevance in investment guidance. However, the paper would benefit from empirical validation of the RBPA system through backtesting in real-world or simulated market conditions. This could involve measuring recommendation accuracy, response latency, and retrieval precision under various market scenarios, thus strengthening the paper’s claims. Additionally, comparing RBPA's recommendations with those of financial experts or existing investment tools would provide further context for evaluating its effectiveness and value. Overall, with robust empirical evidence, this paper could make a significant contribution to AI-driven investment advisory, offering data-rich, actionable financial support tailored to individual investors.
rating: 7
confidence: 3 |
LzN9fYdPpg | 【Proposal】RLOJF: Enhancing LLMs in Olympiad Programming with Online Judge Feedback | [
"Zihan Wang",
"Jiajun Xu",
"Lei Wu"
] | Large Language Models (LLMs) have achieved significant success in programming tasks, particularly excelling on interview-oriented platforms like LeetCode. However, we observe that these models still underperform on more complex problems in Olympiad level competitions. This performance gap primarily stems from the deep mathematical reasoning, complex algorithmic thinking, and diverse solution strategies required for Olympiad programming problems. To address this issue, we propose a novel approach: Reinforcement Learning with Online Judge Feedback (RLOJF). This method simulates the iterative process in real programming environments, allowing the model to dynamically adjust its output based on scores and error messages provided by Online Judge (OJ) systems. RLOJF aims to improve the correctness and efficiency of code generated by the model, develop its ability to iteratively refine code using automated feedback, and enhance its reasoning and problem-solving capabilities in complex programming tasks. Our research contributions include: proposing a new reinforcement learning framework for complex programming tasks, designing a training methodology utilizing OJ feedback, conducting extensive experiments on a large number of complex programming problems to validate the method's effectiveness. | [
"Large Language Models",
"Olympiad Programming",
"Reinforcement Learning",
"Online Judge",
"Code Generation"
] | https://openreview.net/pdf?id=LzN9fYdPpg | tByfLBWr0M | official_review | 1,731,182,103,902 | LzN9fYdPpg | [
"everyone"
] | [
"~Lu_Fan_DB1"
] | title: Review of the Proposal: "Reinforcement Learning for Code Synthesis"
review: This proposal outlines an approach to enhance code synthesis capabilities in large language models (LLMs) for complex, Olympiad-level programming tasks. The proposed framework, Reinforcement Learning with Online Judge Feedback (RLOJF), aims to iteratively refine code generation through feedback from an Online Judge (OJ) system, improving code correctness, efficiency, and problem-solving skills in challenging programming contexts.
rating: 9
confidence: 4 |
LzN9fYdPpg | 【Proposal】RLOJF: Enhancing LLMs in Olympiad Programming with Online Judge Feedback | [
"Zihan Wang",
"Jiajun Xu",
"Lei Wu"
] | Large Language Models (LLMs) have achieved significant success in programming tasks, particularly excelling on interview-oriented platforms like LeetCode. However, we observe that these models still underperform on more complex problems in Olympiad level competitions. This performance gap primarily stems from the deep mathematical reasoning, complex algorithmic thinking, and diverse solution strategies required for Olympiad programming problems. To address this issue, we propose a novel approach: Reinforcement Learning with Online Judge Feedback (RLOJF). This method simulates the iterative process in real programming environments, allowing the model to dynamically adjust its output based on scores and error messages provided by Online Judge (OJ) systems. RLOJF aims to improve the correctness and efficiency of code generated by the model, develop its ability to iteratively refine code using automated feedback, and enhance its reasoning and problem-solving capabilities in complex programming tasks. Our research contributions include: proposing a new reinforcement learning framework for complex programming tasks, designing a training methodology utilizing OJ feedback, conducting extensive experiments on a large number of complex programming problems to validate the method's effectiveness. | [
"Large Language Models",
"Olympiad Programming",
"Reinforcement Learning",
"Online Judge",
"Code Generation"
] | https://openreview.net/pdf?id=LzN9fYdPpg | o2MK288d3s | official_review | 1,731,328,162,262 | LzN9fYdPpg | [
"everyone"
] | [
"~Yu_Zhang61"
] | title: Review of "RLOJF: Enhancing LLMs in Olympiad Programming with Online Judge Feedback"
review: The "Mining Misconception in Mathematics" proposal outlines a novel approach for improving large language models' (LLMs) capacity to classify and understand mathematical misconceptions. By leveraging a combination of zero-shot inference, in-context learning, and fine-tuning, the authors aim to advance LLMs in diagnosing misconceptions from multiple-choice questions, where distractors are often tied to common errors. The model will employ a multi-vector embedding retrieval system to categorize misconceptions into a remarkably granular set of 2,586 categories, which could significantly improve educational assessments by providing detailed insights into students’ misunderstandings. This is an ambitious and potentially impactful project, though it would benefit from a clearer description of how the fine-tuning and in-context learning components will interact with multi-vector retrieval in practice. Additionally, the proposal could elaborate on how mean average precision will be used to interpret the model's performance meaningfully. Addressing these areas would improve the proposal's clarity, feasibility, and contribution to educational technology.
rating: 10
confidence: 4 |
LzN9fYdPpg | 【Proposal】RLOJF: Enhancing LLMs in Olympiad Programming with Online Judge Feedback | [
"Zihan Wang",
"Jiajun Xu",
"Lei Wu"
] | Large Language Models (LLMs) have achieved significant success in programming tasks, particularly excelling on interview-oriented platforms like LeetCode. However, we observe that these models still underperform on more complex problems in Olympiad level competitions. This performance gap primarily stems from the deep mathematical reasoning, complex algorithmic thinking, and diverse solution strategies required for Olympiad programming problems. To address this issue, we propose a novel approach: Reinforcement Learning with Online Judge Feedback (RLOJF). This method simulates the iterative process in real programming environments, allowing the model to dynamically adjust its output based on scores and error messages provided by Online Judge (OJ) systems. RLOJF aims to improve the correctness and efficiency of code generated by the model, develop its ability to iteratively refine code using automated feedback, and enhance its reasoning and problem-solving capabilities in complex programming tasks. Our research contributions include: proposing a new reinforcement learning framework for complex programming tasks, designing a training methodology utilizing OJ feedback, conducting extensive experiments on a large number of complex programming problems to validate the method's effectiveness. | [
"Large Language Models",
"Olympiad Programming",
"Reinforcement Learning",
"Online Judge",
"Code Generation"
] | https://openreview.net/pdf?id=LzN9fYdPpg | nk0cPQXwgh | official_review | 1,731,248,495,119 | LzN9fYdPpg | [
"everyone"
] | [
"~Chentian_wei1"
] | title: The RLOJF framework in this article is clear and the task objectives are well-defined, but the introduction of multiple hyperparameters may lead to ambiguous feedback goals.
review: The RLOJF framework in this article is clear. The description of the shortcomings of the existing tasks is also very specific, making the overall task objectives very clear. If some specific cases could be provided, the effect might be better. Additionally, the reward function seems to introduce many hyperparameters, and I am not sure whether this will lead to certain issues, nor whether it might make the overall feedback objectives unclear.
rating: 8
confidence: 3 |
LzN9fYdPpg | 【Proposal】RLOJF: Enhancing LLMs in Olympiad Programming with Online Judge Feedback | [
"Zihan Wang",
"Jiajun Xu",
"Lei Wu"
] | Large Language Models (LLMs) have achieved significant success in programming tasks, particularly excelling on interview-oriented platforms like LeetCode. However, we observe that these models still underperform on more complex problems in Olympiad level competitions. This performance gap primarily stems from the deep mathematical reasoning, complex algorithmic thinking, and diverse solution strategies required for Olympiad programming problems. To address this issue, we propose a novel approach: Reinforcement Learning with Online Judge Feedback (RLOJF). This method simulates the iterative process in real programming environments, allowing the model to dynamically adjust its output based on scores and error messages provided by Online Judge (OJ) systems. RLOJF aims to improve the correctness and efficiency of code generated by the model, develop its ability to iteratively refine code using automated feedback, and enhance its reasoning and problem-solving capabilities in complex programming tasks. Our research contributions include: proposing a new reinforcement learning framework for complex programming tasks, designing a training methodology utilizing OJ feedback, conducting extensive experiments on a large number of complex programming problems to validate the method's effectiveness. | [
"Large Language Models",
"Olympiad Programming",
"Reinforcement Learning",
"Online Judge",
"Code Generation"
] | https://openreview.net/pdf?id=LzN9fYdPpg | hWpG08Itny | official_review | 1,731,082,383,735 | LzN9fYdPpg | [
"everyone"
] | [
"~Yanchen_Wu1"
] | title: A good idea
review: We all know that the learning of stock data is approaching the bottleneck, and it is a good research idea to use reinforcement learning to assist the training of large models. In this paper, the author designs a scoring criterion as a reward for reinforcement learning to guide large model training, which is very interesting.
rating: 8
confidence: 4 |
LzN9fYdPpg | 【Proposal】RLOJF: Enhancing LLMs in Olympiad Programming with Online Judge Feedback | [
"Zihan Wang",
"Jiajun Xu",
"Lei Wu"
] | Large Language Models (LLMs) have achieved significant success in programming tasks, particularly excelling on interview-oriented platforms like LeetCode. However, we observe that these models still underperform on more complex problems in Olympiad level competitions. This performance gap primarily stems from the deep mathematical reasoning, complex algorithmic thinking, and diverse solution strategies required for Olympiad programming problems. To address this issue, we propose a novel approach: Reinforcement Learning with Online Judge Feedback (RLOJF). This method simulates the iterative process in real programming environments, allowing the model to dynamically adjust its output based on scores and error messages provided by Online Judge (OJ) systems. RLOJF aims to improve the correctness and efficiency of code generated by the model, develop its ability to iteratively refine code using automated feedback, and enhance its reasoning and problem-solving capabilities in complex programming tasks. Our research contributions include: proposing a new reinforcement learning framework for complex programming tasks, designing a training methodology utilizing OJ feedback, conducting extensive experiments on a large number of complex programming problems to validate the method's effectiveness. | [
"Large Language Models",
"Olympiad Programming",
"Reinforcement Learning",
"Online Judge",
"Code Generation"
] | https://openreview.net/pdf?id=LzN9fYdPpg | aFl2X2ROTe | official_review | 1,731,229,355,923 | LzN9fYdPpg | [
"everyone"
] | [
"~Zou_Dongchen1"
] | title: The authors aim to develop a new reinforcement learning framework RLOJF. This is of high practical value!
review: In this proposal, the authors aim to develop a new reinforcement learning framework utilizing OJ feedback. This new framework can handle complex programming problems, dynamically learns in real time, and therefore is of high practical value. Specifically, the authors claim that traditional ML uses supervised learning, but they will use reinforcement learning that offers AI with more flexibilities. The proposal is well written. Good job!
My suggestion for the authors is to consider the following two questions in future works;
1) what are the differences between RLOJF and existing code generation frameworks that use reinforcement learning?
2) What are the innovations and differences of RLOJF compared to the common RLHF techniques?
rating: 9
confidence: 4 |
LzN9fYdPpg | 【Proposal】RLOJF: Enhancing LLMs in Olympiad Programming with Online Judge Feedback | [
"Zihan Wang",
"Jiajun Xu",
"Lei Wu"
] | Large Language Models (LLMs) have achieved significant success in programming tasks, particularly excelling on interview-oriented platforms like LeetCode. However, we observe that these models still underperform on more complex problems in Olympiad level competitions. This performance gap primarily stems from the deep mathematical reasoning, complex algorithmic thinking, and diverse solution strategies required for Olympiad programming problems. To address this issue, we propose a novel approach: Reinforcement Learning with Online Judge Feedback (RLOJF). This method simulates the iterative process in real programming environments, allowing the model to dynamically adjust its output based on scores and error messages provided by Online Judge (OJ) systems. RLOJF aims to improve the correctness and efficiency of code generated by the model, develop its ability to iteratively refine code using automated feedback, and enhance its reasoning and problem-solving capabilities in complex programming tasks. Our research contributions include: proposing a new reinforcement learning framework for complex programming tasks, designing a training methodology utilizing OJ feedback, conducting extensive experiments on a large number of complex programming problems to validate the method's effectiveness. | [
"Large Language Models",
"Olympiad Programming",
"Reinforcement Learning",
"Online Judge",
"Code Generation"
] | https://openreview.net/pdf?id=LzN9fYdPpg | aEgZL51RgV | official_review | 1,731,413,615,359 | LzN9fYdPpg | [
"everyone"
] | [
"~Gangxin_Xu1"
] | title: Review of "RLOJF: Enhancing LLMs in Olympiad Programming with Online Judge Feedback"
review: his proposal introduces an innovative framework, RLOJF, aimed at improving large language models' (LLMs) performance in Olympiad-level programming by integrating reinforcement learning (RL) with feedback from online judge (OJ) systems. The authors identify a gap in current LLMs' abilities to handle complex programming tasks due to the high level of mathematical reasoning and diverse algorithms required in Olympiad competitions. By using OJ feedback, the RLOJF framework iteratively refines generated code based on scoring and error feedback, simulating a real-world programming environment.
Strengths:
Innovative Use of Reinforcement Learning: The proposal’s use of OJ feedback as a reinforcement learning mechanism allows models to refine their problem-solving skills iteratively, closely mirroring a human coder’s debugging process.
Addresses a Notable Gap: The focus on Olympiad-level tasks, which require advanced reasoning beyond typical coding platforms, highlights a clear and relevant challenge in LLM capabilities.
Extensive Experimentation: The proposal’s commitment to conducting large-scale experiments demonstrates the authors’ intent to rigorously validate RLOJF’s effectiveness.
rating: 9
confidence: 5 |
LzN9fYdPpg | 【Proposal】RLOJF: Enhancing LLMs in Olympiad Programming with Online Judge Feedback | [
"Zihan Wang",
"Jiajun Xu",
"Lei Wu"
] | Large Language Models (LLMs) have achieved significant success in programming tasks, particularly excelling on interview-oriented platforms like LeetCode. However, we observe that these models still underperform on more complex problems in Olympiad level competitions. This performance gap primarily stems from the deep mathematical reasoning, complex algorithmic thinking, and diverse solution strategies required for Olympiad programming problems. To address this issue, we propose a novel approach: Reinforcement Learning with Online Judge Feedback (RLOJF). This method simulates the iterative process in real programming environments, allowing the model to dynamically adjust its output based on scores and error messages provided by Online Judge (OJ) systems. RLOJF aims to improve the correctness and efficiency of code generated by the model, develop its ability to iteratively refine code using automated feedback, and enhance its reasoning and problem-solving capabilities in complex programming tasks. Our research contributions include: proposing a new reinforcement learning framework for complex programming tasks, designing a training methodology utilizing OJ feedback, conducting extensive experiments on a large number of complex programming problems to validate the method's effectiveness. | [
"Large Language Models",
"Olympiad Programming",
"Reinforcement Learning",
"Online Judge",
"Code Generation"
] | https://openreview.net/pdf?id=LzN9fYdPpg | IjmNY6bv8N | official_review | 1,731,325,704,346 | LzN9fYdPpg | [
"everyone"
] | [
"~Changsong_Lei2"
] | title: Review of "RLOJF: Enhancing LLMs in Olympiad Programming with Online Judge Feedback"
review: ### Summary:
the proposal presents a new approach, Reinforcement Learning with Online Judge Feedback (RLOJF), to improve large language models (LLMs) in solving complex, Olympiad-level programming tasks.
### Pros:
- The RLOJF framework is innovative, combining reinforcement learning and real-time feedback from OJ systems to address the unique challenges of Olympiad-level programming, which involves deep reasoning and optimization.
- The proposal defines a clear reward function, balancing factors like correctness, performance, and efficiency, which aligns well with the requirements of competitive programming.
### Cons:
- The proposal lacks detailed information on how the model's improvement will be rigorously evaluated across different task complexities, which is essential to assess the overall effectiveness of RLOJF.
rating: 8
confidence: 4 |
LzN9fYdPpg | 【Proposal】RLOJF: Enhancing LLMs in Olympiad Programming with Online Judge Feedback | [
"Zihan Wang",
"Jiajun Xu",
"Lei Wu"
] | Large Language Models (LLMs) have achieved significant success in programming tasks, particularly excelling on interview-oriented platforms like LeetCode. However, we observe that these models still underperform on more complex problems in Olympiad level competitions. This performance gap primarily stems from the deep mathematical reasoning, complex algorithmic thinking, and diverse solution strategies required for Olympiad programming problems. To address this issue, we propose a novel approach: Reinforcement Learning with Online Judge Feedback (RLOJF). This method simulates the iterative process in real programming environments, allowing the model to dynamically adjust its output based on scores and error messages provided by Online Judge (OJ) systems. RLOJF aims to improve the correctness and efficiency of code generated by the model, develop its ability to iteratively refine code using automated feedback, and enhance its reasoning and problem-solving capabilities in complex programming tasks. Our research contributions include: proposing a new reinforcement learning framework for complex programming tasks, designing a training methodology utilizing OJ feedback, conducting extensive experiments on a large number of complex programming problems to validate the method's effectiveness. | [
"Large Language Models",
"Olympiad Programming",
"Reinforcement Learning",
"Online Judge",
"Code Generation"
] | https://openreview.net/pdf?id=LzN9fYdPpg | Gv3111ORYb | official_review | 1,731,394,582,055 | LzN9fYdPpg | [
"everyone"
] | [
"~Wanlan_Ren1"
] | title: Review of RLOJF: Enhancing LLMs in Olympiad Programming with Online Judge Feedback
review: The work is original and significant because it bridges the gap between AI code generation and the demands of high-level competitive programming. By defining a comprehensive state and action space and formulating a detailed reward function, the authors provide a robust framework for future research. Pros of the paper include its novel methodology, the introduction of a Chinese OI dataset for broader evaluation, and the potential to enhance AI's problem-solving capabilities. However, some cons are the potential scalability issues due to reliance on high-performance OJ systems and a need for more in-depth analysis of the reward function's impact on learning outcomes. Overall, the paper makes a valuable contribution to the field of AI-assisted programming.
rating: 9
confidence: 4 |
LzN9fYdPpg | 【Proposal】RLOJF: Enhancing LLMs in Olympiad Programming with Online Judge Feedback | [
"Zihan Wang",
"Jiajun Xu",
"Lei Wu"
] | Large Language Models (LLMs) have achieved significant success in programming tasks, particularly excelling on interview-oriented platforms like LeetCode. However, we observe that these models still underperform on more complex problems in Olympiad level competitions. This performance gap primarily stems from the deep mathematical reasoning, complex algorithmic thinking, and diverse solution strategies required for Olympiad programming problems. To address this issue, we propose a novel approach: Reinforcement Learning with Online Judge Feedback (RLOJF). This method simulates the iterative process in real programming environments, allowing the model to dynamically adjust its output based on scores and error messages provided by Online Judge (OJ) systems. RLOJF aims to improve the correctness and efficiency of code generated by the model, develop its ability to iteratively refine code using automated feedback, and enhance its reasoning and problem-solving capabilities in complex programming tasks. Our research contributions include: proposing a new reinforcement learning framework for complex programming tasks, designing a training methodology utilizing OJ feedback, conducting extensive experiments on a large number of complex programming problems to validate the method's effectiveness. | [
"Large Language Models",
"Olympiad Programming",
"Reinforcement Learning",
"Online Judge",
"Code Generation"
] | https://openreview.net/pdf?id=LzN9fYdPpg | F3DU43h8Gk | official_review | 1,730,956,507,696 | LzN9fYdPpg | [
"everyone"
] | [
"~Iat_Long_Iong1"
] | title: Review of RLOJF
review: This proposal introduces RLOJF, a novel RL framework for code synthesis that leverages OJ feedback to improve LLM performance on complex tasks. Its strengths lie in its novelty, practicality, comprehensive reward function, and focus on Chinese programming. With further exploration and experimentation, RLOJF has the potential to significantly advance the field of code synthesis.
Overall, this proposal outlines a promising code synthesis research plan, offering a clear goal, defined methodology, and innovative use of OJ feedback, warranting further study.
rating: 9
confidence: 4 |
LzN9fYdPpg | 【Proposal】RLOJF: Enhancing LLMs in Olympiad Programming with Online Judge Feedback | [
"Zihan Wang",
"Jiajun Xu",
"Lei Wu"
] | Large Language Models (LLMs) have achieved significant success in programming tasks, particularly excelling on interview-oriented platforms like LeetCode. However, we observe that these models still underperform on more complex problems in Olympiad level competitions. This performance gap primarily stems from the deep mathematical reasoning, complex algorithmic thinking, and diverse solution strategies required for Olympiad programming problems. To address this issue, we propose a novel approach: Reinforcement Learning with Online Judge Feedback (RLOJF). This method simulates the iterative process in real programming environments, allowing the model to dynamically adjust its output based on scores and error messages provided by Online Judge (OJ) systems. RLOJF aims to improve the correctness and efficiency of code generated by the model, develop its ability to iteratively refine code using automated feedback, and enhance its reasoning and problem-solving capabilities in complex programming tasks. Our research contributions include: proposing a new reinforcement learning framework for complex programming tasks, designing a training methodology utilizing OJ feedback, conducting extensive experiments on a large number of complex programming problems to validate the method's effectiveness. | [
"Large Language Models",
"Olympiad Programming",
"Reinforcement Learning",
"Online Judge",
"Code Generation"
] | https://openreview.net/pdf?id=LzN9fYdPpg | C3f1Z1MDOo | official_review | 1,731,336,560,296 | LzN9fYdPpg | [
"everyone"
] | [
"~Jiaxiang_Liu7"
] | title: Ambitious Proposal with Practical Potential
review: This proposal introduces an innovative Reinforcement Learning with Online Judge Feedback (RLOJF) framework designed to enhance code synthesis for complex, Olympiad-level programming tasks. By integrating real-time feedback from Online Judge (OJ) systems, the approach allows for iterative improvement in code correctness and efficiency, closely simulating human learning patterns. The RLOJF framework’s use of a dynamic reward function that balances correctness, efficiency, and resource usage is particularly promising for advancing AI-driven code synthesis. However, the proposal would benefit from additional clarification on the model training process, especially in terms of handling diverse error types from the OJ feedback. Additionally, including initial validation results could strengthen the feasibility of the approach. Overall, this work holds significant potential for elevating automated code generation in challenging coding environments.
rating: 8
confidence: 4 |
LzN9fYdPpg | 【Proposal】RLOJF: Enhancing LLMs in Olympiad Programming with Online Judge Feedback | [
"Zihan Wang",
"Jiajun Xu",
"Lei Wu"
] | Large Language Models (LLMs) have achieved significant success in programming tasks, particularly excelling on interview-oriented platforms like LeetCode. However, we observe that these models still underperform on more complex problems in Olympiad level competitions. This performance gap primarily stems from the deep mathematical reasoning, complex algorithmic thinking, and diverse solution strategies required for Olympiad programming problems. To address this issue, we propose a novel approach: Reinforcement Learning with Online Judge Feedback (RLOJF). This method simulates the iterative process in real programming environments, allowing the model to dynamically adjust its output based on scores and error messages provided by Online Judge (OJ) systems. RLOJF aims to improve the correctness and efficiency of code generated by the model, develop its ability to iteratively refine code using automated feedback, and enhance its reasoning and problem-solving capabilities in complex programming tasks. Our research contributions include: proposing a new reinforcement learning framework for complex programming tasks, designing a training methodology utilizing OJ feedback, conducting extensive experiments on a large number of complex programming problems to validate the method's effectiveness. | [
"Large Language Models",
"Olympiad Programming",
"Reinforcement Learning",
"Online Judge",
"Code Generation"
] | https://openreview.net/pdf?id=LzN9fYdPpg | ADdO8wOfzq | official_review | 1,731,316,507,460 | LzN9fYdPpg | [
"everyone"
] | [
"~Yida_Lu1"
] | title: Clear framework and good idea
review: This study proposes RLOJF, a new framework to improve LLMs' ability to solve complex code problems. The framework incorporate OJ systems to construct the reward function, which provides a new perspective and a convenient method to optimize LLMs' code ability. The task definition and the framework design are clear, and the proposal is well-structured. On the other hand, the proposal can be further strengthened by comparing RLOJF with existing baselines that improve LLMs' coding ability to demonstrate the effectiveness of this framework.
rating: 9
confidence: 4 |
LzN9fYdPpg | 【Proposal】RLOJF: Enhancing LLMs in Olympiad Programming with Online Judge Feedback | [
"Zihan Wang",
"Jiajun Xu",
"Lei Wu"
] | Large Language Models (LLMs) have achieved significant success in programming tasks, particularly excelling on interview-oriented platforms like LeetCode. However, we observe that these models still underperform on more complex problems in Olympiad level competitions. This performance gap primarily stems from the deep mathematical reasoning, complex algorithmic thinking, and diverse solution strategies required for Olympiad programming problems. To address this issue, we propose a novel approach: Reinforcement Learning with Online Judge Feedback (RLOJF). This method simulates the iterative process in real programming environments, allowing the model to dynamically adjust its output based on scores and error messages provided by Online Judge (OJ) systems. RLOJF aims to improve the correctness and efficiency of code generated by the model, develop its ability to iteratively refine code using automated feedback, and enhance its reasoning and problem-solving capabilities in complex programming tasks. Our research contributions include: proposing a new reinforcement learning framework for complex programming tasks, designing a training methodology utilizing OJ feedback, conducting extensive experiments on a large number of complex programming problems to validate the method's effectiveness. | [
"Large Language Models",
"Olympiad Programming",
"Reinforcement Learning",
"Online Judge",
"Code Generation"
] | https://openreview.net/pdf?id=LzN9fYdPpg | 5yE3WOBGb4 | official_review | 1,731,299,777,296 | LzN9fYdPpg | [
"everyone"
] | [
"~Feihong_Zhang1"
] | title: RLOJF: Enhancing LLMs in Olympiad Programming with Online Judge Feedback
review: This paper presents an innovative approach to enhancing code synthesis capabilities by incorporating reinforcement learning with online judge feedback (RLOJF). The authors aim to bridge the gap between typical coding tasks and the more challenging demands of Olympiad-level problems, focusing on complex reasoning and iterative improvement through real-time feedback. The proposed RLOJF framework shows promise in improving model adaptability and solution accuracy, particularly for high-complexity programming tasks.
Strengths
Novel Methodology: The RLOJF framework creatively combines reinforcement learning with dynamic feedback from online judge systems, enabling iterative refinement in code synthesis.
Clear Motivation: The authors provide a strong rationale for why Olympiad-level coding tasks require a distinct approach, highlighting the limitations of existing methods.
Practical Evaluation Setup: Using competitive programming datasets and real-time feedback mechanisms adds practical relevance and rigor to the evaluation.
Areas for Improvement
Experimental Details: More specifics on evaluation metrics and baseline comparisons would enhance the paper's clarity.
Computational Efficiency: A brief discussion on the resource demands of the RLOJF framework could clarify its feasibility for broader applications.
rating: 8
confidence: 4 |
L6l5hYhLFc | 【Proposal】 | [
"Kehan Zheng",
"Yida Lu",
"Wenjing Wu"
] | the proposal of the project | [
"set question",
"text generation",
"RAG",
"SFT"
] | https://openreview.net/pdf?id=L6l5hYhLFc | tR7llDPdq9 | official_review | 1,731,424,808,109 | L6l5hYhLFc | [
"everyone"
] | [
"~Kuanghao_Wang1"
] | title: Good direction
review: The research for this proposal is on AI teaching assistants, which argues that science and engineering undergraduates face challenges in getting enough personalised practice, which prevents them from deepening their understanding and improving their academic performance, and that AI teaching assistants can be an effective solution to this problem. This proposal will be based on Chat-GLM, using RAG and SFT methods to improve the task-specific performance and generalisation of the model. The paper describes the problem more clearly and gives more specific follow-up steps. However, it should be noted that there are already many AI assistants on the market, and this paper should highlight what advantages and shortcomings the results of this paper will have compared to the existing ones. In addition, the format of the article is slightly problematic, and needs to be supplemented with a clearer title.
rating: 8
confidence: 4 |
L6l5hYhLFc | 【Proposal】 | [
"Kehan Zheng",
"Yida Lu",
"Wenjing Wu"
] | the proposal of the project | [
"set question",
"text generation",
"RAG",
"SFT"
] | https://openreview.net/pdf?id=L6l5hYhLFc | qn4PNCX6yn | official_review | 1,731,401,475,904 | L6l5hYhLFc | [
"everyone"
] | [
"~Kaiwei_Zhang3"
] | title: Has pratical value, yet lacks novelty and difficulty
review: **1. Summary:**
The proposal aims to develop an AI-powered teaching assistant using the GLM-4 model to provide personalized practice questions for university-level courses, initially focusing on Physics and Chemical Engineering Thermodynamics. It highlights the need for individualized practice in challenging subjects, where traditional methods lack tailored feedback for large student groups.
**2. Clarity:**
The paper is fairly clear on its purpose. However, it would be better to furthur illustrate on how to improve the performance of AI teaching assistants.
**3. Originality:**
The idea of training an AI assistant for classes has long been proposed, and various products have been published.
**4. Significance:**
Using AI to assist education and teaching is a very hot subject nowadays, and is of great significance since it could leverage the inequality of educational resources, improve studying efficiency, and reduce the burden of teachers.
**5. Pros:**
* The project focuses on a practical problem, and could potentially be used to aid the studying of students in Chemical Thermodynamics and University Physics classes.
**6. Cons:**
* There are already a huge amount of AI teaching assistants. It would be better to summarize current developments in this field and analyze the weakness of existing products.
* The project is fairly easy. Fintuning LLMs or doing RAGs is not considered a complex task.
* The authors point out that "current models often face significant issues, such as missing variables and poor association with the intended knowledge points." However, they do not explain how to solve these existing problems. Furthur investigations about SFT and RAG techniques are needed.
rating: 6
confidence: 5 |
L6l5hYhLFc | 【Proposal】 | [
"Kehan Zheng",
"Yida Lu",
"Wenjing Wu"
] | the proposal of the project | [
"set question",
"text generation",
"RAG",
"SFT"
] | https://openreview.net/pdf?id=L6l5hYhLFc | mxo1rj0jQ5 | official_review | 1,731,295,542,850 | L6l5hYhLFc | [
"everyone"
] | [
"~Junjie_Chen1"
] | title: Good Work
review: The proposal effectively addresses a crucial challenge in higher education by proposing an AI-powered teaching assistant to provide personalized practice for students. Its structure is clear, and the focus on using advanced models like GLM-4 demonstrates feasibility. The inclusion of methods such as Retrieval-Augmented Generation (RAG) and Supervised Fine-Tuning (SFT) aligns well with the stated goals, and the proposal is grounded in practical applications with rich datasets from university-level courses.
However, the baselines provided, such as the use of GLM-4-9B-Chat, seem limited for comparison and lack diversity in competing approaches. Additional baselines incorporating alternative models or methodologies would strengthen the evaluation.
rating: 8
confidence: 4 |
L6l5hYhLFc | 【Proposal】 | [
"Kehan Zheng",
"Yida Lu",
"Wenjing Wu"
] | the proposal of the project | [
"set question",
"text generation",
"RAG",
"SFT"
] | https://openreview.net/pdf?id=L6l5hYhLFc | kAOTJCzI0O | official_review | 1,731,331,453,286 | L6l5hYhLFc | [
"everyone"
] | [
"~Yang_Ouyang2"
] | title: Great background research and methodology, but does not address scaling and privacy concerns.
review: Strengths
Clear Motivation and objectives: The projects has a clear purpose and motivation.
Solid Background: The background section and related work are well-researched.
Detailed Methodology and Evaluation Plan.
Weaknesses
Personalization Mechanism: Need more details on the personalization mechanism
Data Privacy: how and what data will be collected?
The proposal shows a promising solution for personalized learning, but could improve by addressing the personalization mechanism, data privacy, and scalability.
rating: 9
confidence: 5 |
L6l5hYhLFc | 【Proposal】 | [
"Kehan Zheng",
"Yida Lu",
"Wenjing Wu"
] | the proposal of the project | [
"set question",
"text generation",
"RAG",
"SFT"
] | https://openreview.net/pdf?id=L6l5hYhLFc | iymS8YNJOv | official_review | 1,731,259,739,852 | L6l5hYhLFc | [
"everyone"
] | [
"~Keyu_Shen1"
] | title: Good Proposal
review: The proposal aims to develop a system that generates customized questions tailored to individual learning needs, leveraging techniques such as Retrieval-Augmented Generation (RAG) and Supervised Fine-Tuning (SFT). Strengths of the proposal include its clear relevance to current educational challenges, and the integration of RAG and SFT demonstrates the use of advanced techniques to align the model with educational tasks. However, the proposal could benefit from a more explicit discussion on potential challenges, such as data insufficiency, the quality of generated questions, and the design of evaluation metrics. Additionally, the proposal could establish its novelty more clearly to distinguish its contributions within the field.
rating: 8
confidence: 3 |
L6l5hYhLFc | 【Proposal】 | [
"Kehan Zheng",
"Yida Lu",
"Wenjing Wu"
] | the proposal of the project | [
"set question",
"text generation",
"RAG",
"SFT"
] | https://openreview.net/pdf?id=L6l5hYhLFc | hAIYDK7hyU | official_review | 1,731,317,579,988 | L6l5hYhLFc | [
"everyone"
] | [
"~Tianhai_Liang1"
] | title: Good Proposal
review: The proposal aims to develop an AI-powered teaching assistant using ChatGLM to provide personalized practice for university courses like Physics. It uses Retrieval-Augmented Generation (RAG) and Supervised Fine-Tuning (SFT) to improve question generation and includes automated evaluation for quality control.
The method supports personalized learning with adaptive questions, improving student engagement and learning efficiency. It leverages advanced AI techniques to optimize education, making practice more interactive and scalable.
The approach requires large labeled datasets for SFT, which can be hard to obtain. There’s also a risk of generating off-target content, and it demands substantial technical resources, raising costs and complexity. Also, this proposal would be better if there was a clear title and abstract.
rating: 8
confidence: 4 |
L6l5hYhLFc | 【Proposal】 | [
"Kehan Zheng",
"Yida Lu",
"Wenjing Wu"
] | the proposal of the project | [
"set question",
"text generation",
"RAG",
"SFT"
] | https://openreview.net/pdf?id=L6l5hYhLFc | ZHWuh6NTe2 | official_review | 1,731,316,677,310 | L6l5hYhLFc | [
"everyone"
] | [
"~Nan_Sun10"
] | title: Ambitious but Unrefined: An AI-Powered Teaching Assistant Proposal for Personalized Student Practice
review: This proposal introduces an AI-powered teaching assistant based on the GLM-4 series models, aimed at generating personalized practice questions for university students in physics and chemical engineering thermodynamics.
The idea holds significant promise, particularly in addressing the lack of individualized practice in large classroom settings, which often leads to gaps in student understanding. By leveraging techniques like Retrieval-Augmented Generation and Supervised Fine-Tuning, the system aspires to align generated questions with specific knowledge points and student needs.
However, the proposal lacks a detailed plan for scaling the solution across diverse academic subjects and adapting to individual student progress over time. The reliance on RAG and SFT, while effective, might not fully address the intricacies of maintaining question quality and consistency without further enhancements. Additionally, the evaluation metrics are not fully developed, which could lead to challenges in objectively assessing the effectiveness of generated content.
rating: 8
confidence: 3 |
L6l5hYhLFc | 【Proposal】 | [
"Kehan Zheng",
"Yida Lu",
"Wenjing Wu"
] | the proposal of the project | [
"set question",
"text generation",
"RAG",
"SFT"
] | https://openreview.net/pdf?id=L6l5hYhLFc | Hl8l6nI5ui | official_review | 1,731,413,882,582 | L6l5hYhLFc | [
"everyone"
] | [
"~Maanping_Shao1"
] | title: Review
review: This proposal, presented by researchers from Tsinghua University, introduces an AI-powered teaching assistant leveraging the ChatGLM model to provide personalized practice for university students in challenging subjects like physics and engineering thermodynamics. The assistant generates tailored exercises, addressing gaps in traditional education by offering individualized learning experiences. The proposed methods include Retrieval-Augmented Generation (RAG) and Supervised Fine-Tuning (SFT) to improve alignment with specific educational needs. This approach has the potential to significantly enhance students' understanding by integrating AI-driven, adaptable support into their learning process.
rating: 8
confidence: 3 |
L6l5hYhLFc | 【Proposal】 | [
"Kehan Zheng",
"Yida Lu",
"Wenjing Wu"
] | the proposal of the project | [
"set question",
"text generation",
"RAG",
"SFT"
] | https://openreview.net/pdf?id=L6l5hYhLFc | 4GSn9pNZzg | official_review | 1,731,406,500,694 | L6l5hYhLFc | [
"everyone"
] | [
"~Eddy_Yue1"
] | title: Chem & Phys
review: Due to the large amounts of AI teaching assistants on the rise, the idea of specialising in Uni chem and phys and leveraging ChatGLM to provide targeted practice, along with the focus on practice question generation may give this bot an advantage.
Writing a more elaborate and specific title could definitely go a long way.
rating: 8
confidence: 4 |
L6l5hYhLFc | 【Proposal】 | [
"Kehan Zheng",
"Yida Lu",
"Wenjing Wu"
] | the proposal of the project | [
"set question",
"text generation",
"RAG",
"SFT"
] | https://openreview.net/pdf?id=L6l5hYhLFc | 0ZmDHaCiDR | official_review | 1,731,169,324,041 | L6l5hYhLFc | [
"everyone"
] | [
"~Bryan_Constantine_Sadihin1"
] | title: Review of "Proposal"
review: Strength:
1. Educational Relevance: This proposal innovatively uses AI-powered teaching assistant to targets challenging subjects such as physics and engineering. The success of this project is highly relevant to modern education.
2. Comprehensive Methodological Approach and Evaluation Framework: The proposal outlines a detailed strategy to solve the research problem and evaluation framework.
Weakness:
1. Paper formatting: Some format of the papers can be improved, such as nonexistent research title and abstract.
rating: 9
confidence: 4 |
IL9QnLCJ6u | Harmonic Clarity: Audio Source Separation Techniques on Classical Music | [
"Rim El Filali",
"Ziyad Fawzy",
"zhuzhengyang"
] | Hearing loss affects music perception, often causing quiet passages to become inaudible, instruments to be unidentifiable, lyrics difficult to hear, and pitch to distort. Current hearing aids struggle with complex compositions, especially classical music. This challenge is theoretically and practically important, advancing the performance of Music Source Separation (MSS) on classical music’s compositional complexity, while promoting emotional well-being and social inclusion for the hearing-impaired.
Separating instruments is crucial for effectively rebalancing a music piece. Rebalancing then allows for the creation of personalized remixes, enhancing the listening experience. Our approach involves developing an end-to-end pipeline that separates, rebalances, and remixes classical music, designed to benefit hearing aid users in both live and recorded settings. This system aims to broaden accessibility, creating a tailored auditory experience for users with hearing loss. | [
"Music Source Separation",
"Audio Quality Assessment"
] | https://openreview.net/pdf?id=IL9QnLCJ6u | x2OdkfEbGw | official_review | 1,731,337,807,538 | IL9QnLCJ6u | [
"everyone"
] | [
"~Hector_Rodriguez_Rodriguez1"
] | title: Review "Harmonic Clarity: Audio Source Separation Techniques on Classical Music"
review: The proposal presents a Mamba architecture for music source separation. The introduction provides a compelling case for the need to enhance hearing aids through music source separation. The related work highlights advancements in audio and music source separation, particularly addressing the challenges of separating instruments in small classical ensembles.
The proposed method and evaluation metric are clear and well established. The specialized and combined instrument approach leverages Mamba's strenghts and the evaluation metric is solid. The proposal could be further improved by specifying the data source that will be used for training and testing.
Overall, the proposal is clear, well-written, and meets all requirements.
rating: 10
confidence: 4 |
IL9QnLCJ6u | Harmonic Clarity: Audio Source Separation Techniques on Classical Music | [
"Rim El Filali",
"Ziyad Fawzy",
"zhuzhengyang"
] | Hearing loss affects music perception, often causing quiet passages to become inaudible, instruments to be unidentifiable, lyrics difficult to hear, and pitch to distort. Current hearing aids struggle with complex compositions, especially classical music. This challenge is theoretically and practically important, advancing the performance of Music Source Separation (MSS) on classical music’s compositional complexity, while promoting emotional well-being and social inclusion for the hearing-impaired.
Separating instruments is crucial for effectively rebalancing a music piece. Rebalancing then allows for the creation of personalized remixes, enhancing the listening experience. Our approach involves developing an end-to-end pipeline that separates, rebalances, and remixes classical music, designed to benefit hearing aid users in both live and recorded settings. This system aims to broaden accessibility, creating a tailored auditory experience for users with hearing loss. | [
"Music Source Separation",
"Audio Quality Assessment"
] | https://openreview.net/pdf?id=IL9QnLCJ6u | rKPGndWDbU | official_review | 1,731,389,998,876 | IL9QnLCJ6u | [
"everyone"
] | [
"~Kaiyuan_Zhang6"
] | title: Interesting yet need polish
review: An interesting topic focusing on audio source separation tasks for hearing aids, and well writtened. Some discussions on methods and evaluation have been proposed.
However, as a proposal paper, it is supposed to contain more expectations or future plans. For example, mentioning possible challenges and steps may be a good choice. Besides, more relevant research on the current amount of hearing aids patients need to be cited.
Overall, I would like to give it a 7 points.
rating: 7
confidence: 3 |
IL9QnLCJ6u | Harmonic Clarity: Audio Source Separation Techniques on Classical Music | [
"Rim El Filali",
"Ziyad Fawzy",
"zhuzhengyang"
] | Hearing loss affects music perception, often causing quiet passages to become inaudible, instruments to be unidentifiable, lyrics difficult to hear, and pitch to distort. Current hearing aids struggle with complex compositions, especially classical music. This challenge is theoretically and practically important, advancing the performance of Music Source Separation (MSS) on classical music’s compositional complexity, while promoting emotional well-being and social inclusion for the hearing-impaired.
Separating instruments is crucial for effectively rebalancing a music piece. Rebalancing then allows for the creation of personalized remixes, enhancing the listening experience. Our approach involves developing an end-to-end pipeline that separates, rebalances, and remixes classical music, designed to benefit hearing aid users in both live and recorded settings. This system aims to broaden accessibility, creating a tailored auditory experience for users with hearing loss. | [
"Music Source Separation",
"Audio Quality Assessment"
] | https://openreview.net/pdf?id=IL9QnLCJ6u | i52UOX0q3K | official_review | 1,731,220,826,274 | IL9QnLCJ6u | [
"everyone"
] | [
"~Sui_Yuanpei1"
] | title: Strong concept with potential for impactful applications, though limited practical testing for hearing-impaired users might reduce real-world relevance
review: This proposal presents an innovative approach to Music Source Separation (MSS) specifically for classical music, focusing on improving accessibility for hearing aid users. By utilizing the Mamba model for efficient, adaptive separation, the project addresses both the technical and social aspects of enabling hearing-impaired individuals to enjoy complex compositions. Given the compositional intricacies of classical music, this MSS work is especially significant in promoting inclusivity and enhancing auditory experiences for those with hearing impairments.
Pros:
1.The focus on MSS for classical music, targeting accessibility for hearing-impaired users, adds a valuable social dimension to the technical work.
2.The use of Mamba, known for its efficiency in long-sequence audio tasks, is well-suited for complex music separation and can address overlapping frequency challenges.
3.The proposal’s plan to experiment with both specialized and combined instrument models provides a robust methodology for evaluating MSS performance.
4.By combining HAAQI as an objective metric with perceptual testing for hearing-impaired listeners, the evaluation plan is thorough.
Cons:
1.There is a lack of emphasis on practical, large-scale testing with hearing-impaired users, which may impact the real-world applicability of the findings.
2.The high complexity of separating classical compositions, especially distinguishing similar instruments (e.g., multiple stringed instruments), may lead to performance limitations.
3.The success of the project is heavily dependent on Mamba’s handling of temporal dependencies in overlapping frequencies, which may require significant tuning.
4.Training models specific to individual instruments could limit generalizability across diverse musical compositions.
rating: 8
confidence: 5 |
IL9QnLCJ6u | Harmonic Clarity: Audio Source Separation Techniques on Classical Music | [
"Rim El Filali",
"Ziyad Fawzy",
"zhuzhengyang"
] | Hearing loss affects music perception, often causing quiet passages to become inaudible, instruments to be unidentifiable, lyrics difficult to hear, and pitch to distort. Current hearing aids struggle with complex compositions, especially classical music. This challenge is theoretically and practically important, advancing the performance of Music Source Separation (MSS) on classical music’s compositional complexity, while promoting emotional well-being and social inclusion for the hearing-impaired.
Separating instruments is crucial for effectively rebalancing a music piece. Rebalancing then allows for the creation of personalized remixes, enhancing the listening experience. Our approach involves developing an end-to-end pipeline that separates, rebalances, and remixes classical music, designed to benefit hearing aid users in both live and recorded settings. This system aims to broaden accessibility, creating a tailored auditory experience for users with hearing loss. | [
"Music Source Separation",
"Audio Quality Assessment"
] | https://openreview.net/pdf?id=IL9QnLCJ6u | YCLM906pBc | official_review | 1,731,322,749,118 | IL9QnLCJ6u | [
"everyone"
] | [
"~Zhang_Mingkang1"
] | title: Interesting topic
review: Strengths:
Background:
Compelling societal impact addressing hearing accessibility.
Well-defined problem scope combining ML and audio processing.
Definition :
HAAQI metric clearly defined with formula.
Good explanation of evaluation criteria.
Related Work :
Comprehensive review of audio source separation techniques.
Clear analysis of different architectural approaches (DNN, CNN, etc.).
Proposed Method:
Innovative use of Mamba architecture for audio processing.
Clear experimental configurations (specialized vs combined models).
Well-thought-out evaluation methodology.
Areas for Improvement:
Could provide more mathematical details about the Mamba architecture adaptation.
More specifics on handling real-time processing requirements.
Consider adding perceptual evaluation metrics beyond HAAQI.
rating: 8
confidence: 3 |
IL9QnLCJ6u | Harmonic Clarity: Audio Source Separation Techniques on Classical Music | [
"Rim El Filali",
"Ziyad Fawzy",
"zhuzhengyang"
] | Hearing loss affects music perception, often causing quiet passages to become inaudible, instruments to be unidentifiable, lyrics difficult to hear, and pitch to distort. Current hearing aids struggle with complex compositions, especially classical music. This challenge is theoretically and practically important, advancing the performance of Music Source Separation (MSS) on classical music’s compositional complexity, while promoting emotional well-being and social inclusion for the hearing-impaired.
Separating instruments is crucial for effectively rebalancing a music piece. Rebalancing then allows for the creation of personalized remixes, enhancing the listening experience. Our approach involves developing an end-to-end pipeline that separates, rebalances, and remixes classical music, designed to benefit hearing aid users in both live and recorded settings. This system aims to broaden accessibility, creating a tailored auditory experience for users with hearing loss. | [
"Music Source Separation",
"Audio Quality Assessment"
] | https://openreview.net/pdf?id=IL9QnLCJ6u | NcfERzyYeS | official_review | 1,731,395,037,289 | IL9QnLCJ6u | [
"everyone"
] | [
"~Chumeng_Jiang1"
] | title: Practically meaningful and thoroughly researched
review: This proposal focuses on developing advanced music source separation techniques, especially for classical music, aiming to improve accessibility for hearing-impaired listeners. The project plans to employ a state-space model architecture, Mamba, to enhance music source separation, exploring two specific approaches: specialized models for each instrument and a unified model for all instruments. Evaluation will involve objective metrics such as the Hearing Aid Audio Quality Index (HAAQI) and perceptual tests with hearing-impaired individuals.
Strengths:
- **Interesting and Significant research topic:** This research has real-world applications and can genuinely help people with hearing impairments.
- **Thorough related work:** The authors demonstrate a solid understanding of the field, having conducted detailed research on related work and identified its strengths and weaknesses.
Weaknesses:
- **Unclear methodology:** How will the specialized instrument models and combined instrument model be integrated?
- **Is the introduction of ASS necessary?:** Compared to ASS, MSS has a higher sample rate, but this doesn’t seem to be a critical feature in the proposed algorithm. Therefore, it doesn’t seem necessary to strongly differentiate between ASS and MSS.
rating: 8
confidence: 4 |
IL9QnLCJ6u | Harmonic Clarity: Audio Source Separation Techniques on Classical Music | [
"Rim El Filali",
"Ziyad Fawzy",
"zhuzhengyang"
] | Hearing loss affects music perception, often causing quiet passages to become inaudible, instruments to be unidentifiable, lyrics difficult to hear, and pitch to distort. Current hearing aids struggle with complex compositions, especially classical music. This challenge is theoretically and practically important, advancing the performance of Music Source Separation (MSS) on classical music’s compositional complexity, while promoting emotional well-being and social inclusion for the hearing-impaired.
Separating instruments is crucial for effectively rebalancing a music piece. Rebalancing then allows for the creation of personalized remixes, enhancing the listening experience. Our approach involves developing an end-to-end pipeline that separates, rebalances, and remixes classical music, designed to benefit hearing aid users in both live and recorded settings. This system aims to broaden accessibility, creating a tailored auditory experience for users with hearing loss. | [
"Music Source Separation",
"Audio Quality Assessment"
] | https://openreview.net/pdf?id=IL9QnLCJ6u | NajPtFjGoO | official_review | 1,731,390,897,713 | IL9QnLCJ6u | [
"everyone"
] | [
"~Zhaoxi_Li2"
] | title: Proposal Review: Harmonic Clarity – Audio Source Separation Techniques for Classical Music
review: This proposal presents a well-structured approach to advancing audio source separation (ASS) in classical music, a task with notable challenges due to the complexity and overlapping nature of musical elements in classical compositions. By adapting the Mamba state-space model, which excels at handling long-sequence dependencies and overlapping signals, the authors propose an innovative method that aligns with the needs of hearing aid users by enabling individualized instrument rebalancing. The approach is technically sound, particularly with its dual model configurations (specialized and combined models for instrument separation), which will help assess Mamba’s adaptability for ASS. The evaluation strategy is rigorous, combining the Hearing Aid Audio Quality Index (HAAQI) for objective analysis and perceptual tests for qualitative feedback. However, further details on comparative benchmarks and model optimization strategies would enhance clarity on expected outcomes and practical application potential. Overall, this proposal is promising, addressing a pressing need in audio accessibility with strong potential for impact.
rating: 8
confidence: 3 |
IL9QnLCJ6u | Harmonic Clarity: Audio Source Separation Techniques on Classical Music | [
"Rim El Filali",
"Ziyad Fawzy",
"zhuzhengyang"
] | Hearing loss affects music perception, often causing quiet passages to become inaudible, instruments to be unidentifiable, lyrics difficult to hear, and pitch to distort. Current hearing aids struggle with complex compositions, especially classical music. This challenge is theoretically and practically important, advancing the performance of Music Source Separation (MSS) on classical music’s compositional complexity, while promoting emotional well-being and social inclusion for the hearing-impaired.
Separating instruments is crucial for effectively rebalancing a music piece. Rebalancing then allows for the creation of personalized remixes, enhancing the listening experience. Our approach involves developing an end-to-end pipeline that separates, rebalances, and remixes classical music, designed to benefit hearing aid users in both live and recorded settings. This system aims to broaden accessibility, creating a tailored auditory experience for users with hearing loss. | [
"Music Source Separation",
"Audio Quality Assessment"
] | https://openreview.net/pdf?id=IL9QnLCJ6u | L3Vm8El58x | official_review | 1,731,410,091,049 | IL9QnLCJ6u | [
"everyone"
] | [
"~Xuancheng_Li1"
] | title: review
review: Summary
This proposal addresses the challenges of music perception for hearing-impaired individuals, particularly in classical music where complex compositions often overwhelm standard hearing aids. The authors aim to develop an end-to-end pipeline for Music Source Separation (MSS) that isolates and rebalances individual instruments, enabling the creation of personalized remixes to enhance clarity. This tailored approach could improve accessibility and the listening experience for users in both live and recorded music settings.
Strengths
The project tackles a unique and impactful problem, with a focus on emotional well-being and inclusion for the hearing-impaired. The proposal’s emphasis on rebalancing and remixing demonstrates a thoughtful approach to improving auditory accessibility and enhancing the richness of classical music for those with hearing challenges.
Weaknesses
The complexity of classical compositions may pose challenges for MSS accuracy, and details on specific techniques or models to address this are limited. Additional explanation of how the system will be evaluated in live vs. recorded settings would clarify its practical implementation.
Conclusion
This project offers an innovative approach to enhancing music accessibility for hearing-impaired listeners, with potential to significantly improve their musical experience. Further clarity on the MSS techniques and evaluation methods will be essential to assess the pipeline’s effectiveness in real-world applications.
rating: 9
confidence: 4 |
IL9QnLCJ6u | Harmonic Clarity: Audio Source Separation Techniques on Classical Music | [
"Rim El Filali",
"Ziyad Fawzy",
"zhuzhengyang"
] | Hearing loss affects music perception, often causing quiet passages to become inaudible, instruments to be unidentifiable, lyrics difficult to hear, and pitch to distort. Current hearing aids struggle with complex compositions, especially classical music. This challenge is theoretically and practically important, advancing the performance of Music Source Separation (MSS) on classical music’s compositional complexity, while promoting emotional well-being and social inclusion for the hearing-impaired.
Separating instruments is crucial for effectively rebalancing a music piece. Rebalancing then allows for the creation of personalized remixes, enhancing the listening experience. Our approach involves developing an end-to-end pipeline that separates, rebalances, and remixes classical music, designed to benefit hearing aid users in both live and recorded settings. This system aims to broaden accessibility, creating a tailored auditory experience for users with hearing loss. | [
"Music Source Separation",
"Audio Quality Assessment"
] | https://openreview.net/pdf?id=IL9QnLCJ6u | FbwoFsAvBI | official_review | 1,731,258,261,625 | IL9QnLCJ6u | [
"everyone"
] | [
"~Matteo_Jiahao_Chen1"
] | title: Good proposal for Music Source Separation in classical music
review: This work tackles the challenge of Music Source Separation (MSS) in classical music, aimed at enhancing the listening experience for hearing-impaired users. It introduces an approach based on Mamba.
### Strengths
1. The use of the Mamba model provides an efficient approach to MSS, capable of handling long sequences with bidirectional processing, which enhances separation quality.
2. The authors employ both an objective metric (HAAQI) and perceptual feedback from users with hearing loss, which strengthens the validity of their results.
rating: 10
confidence: 5 |
IL9QnLCJ6u | Harmonic Clarity: Audio Source Separation Techniques on Classical Music | [
"Rim El Filali",
"Ziyad Fawzy",
"zhuzhengyang"
] | Hearing loss affects music perception, often causing quiet passages to become inaudible, instruments to be unidentifiable, lyrics difficult to hear, and pitch to distort. Current hearing aids struggle with complex compositions, especially classical music. This challenge is theoretically and practically important, advancing the performance of Music Source Separation (MSS) on classical music’s compositional complexity, while promoting emotional well-being and social inclusion for the hearing-impaired.
Separating instruments is crucial for effectively rebalancing a music piece. Rebalancing then allows for the creation of personalized remixes, enhancing the listening experience. Our approach involves developing an end-to-end pipeline that separates, rebalances, and remixes classical music, designed to benefit hearing aid users in both live and recorded settings. This system aims to broaden accessibility, creating a tailored auditory experience for users with hearing loss. | [
"Music Source Separation",
"Audio Quality Assessment"
] | https://openreview.net/pdf?id=IL9QnLCJ6u | 3vJys7OkxI | official_review | 1,731,306,928,003 | IL9QnLCJ6u | [
"everyone"
] | [
"~Zihan_Lv1"
] | title: Sufficient research and innovative proposed method
review: The project has great potential to contribute to both MSS research and hearing aid technologies and conducts sufficient research. More experimental validation and discussion on the limitations might be needed given the controversial performance of Mamba.
rating: 8
confidence: 4 |
I2FbCxyYVS | [Proposal-ML]Protein Sequence Generation Model | [
"pan jiang",
"Zeeshan Zulfiqar",
"Ivan Iazykov"
] | This proposal outlines a novel approach for protein sequence generation, a key area in computational biology focused on generating sequences with specific functional and structural characteristics. Despite recent advances with deep learning models, challenges remain in handling long sequences and capturing biological dynamics. We propose implementing a Mamba architecture that leverages selective state-space updates to achieve efficient protein sequence generation. With linear computational complexity and effective handling of long-range dependencies, this architecture is designed to be more suited to biological dynamics while reducing resource requirements. A comprehensive evaluation on the UniRef50 dataset will demonstrate its potential to deliver competitive performance, providing a viable solution for research environments with limited computational resources. | [
"protein sequence generation",
"Mamba architecture",
"efficient modeling",
"protein language models",
"biological dynamics"
] | https://openreview.net/pdf?id=I2FbCxyYVS | z6wpZsDkiw | official_review | 1,731,382,751,110 | I2FbCxyYVS | [
"everyone"
] | [
"~Ruilin_Hu2"
] | title: Proposal of paper "Protein Sequence Generation Model"
review: This proposal effectively explores the implementation of a small-scale Mamba architecture for protein sequence generation, addressing computational efficiency, long-sequence handling, and biological plausibility. The primary strengths include (1) innovative use of selective state-space updates, yielding linear complexity, (2) comprehensive evaluation across structural and functional metrics, and (3) accessibility through reduced computational demands. However, potential weaknesses are (1) limited scalability compared to larger models and (2) reliance on specific datasets, which may affect generalization. Overall, the proposal promises meaningful contributions to resource-efficient protein modeling.
rating: 10
confidence: 4 |
I2FbCxyYVS | [Proposal-ML]Protein Sequence Generation Model | [
"pan jiang",
"Zeeshan Zulfiqar",
"Ivan Iazykov"
] | This proposal outlines a novel approach for protein sequence generation, a key area in computational biology focused on generating sequences with specific functional and structural characteristics. Despite recent advances with deep learning models, challenges remain in handling long sequences and capturing biological dynamics. We propose implementing a Mamba architecture that leverages selective state-space updates to achieve efficient protein sequence generation. With linear computational complexity and effective handling of long-range dependencies, this architecture is designed to be more suited to biological dynamics while reducing resource requirements. A comprehensive evaluation on the UniRef50 dataset will demonstrate its potential to deliver competitive performance, providing a viable solution for research environments with limited computational resources. | [
"protein sequence generation",
"Mamba architecture",
"efficient modeling",
"protein language models",
"biological dynamics"
] | https://openreview.net/pdf?id=I2FbCxyYVS | w2mO1I9Tp4 | official_review | 1,731,049,700,917 | I2FbCxyYVS | [
"everyone"
] | [
"~Peidong_Zhang1"
] | title: Strengths and limitations of proposal
review: This proposal presents a small-scale Mamba architecture for protein sequence generation, focusing on efficient protein modeling with reduced computational resources. The Mamba model is designed to handle long-range dependencies and protein dynamics while maintaining linear complexity, offering a promising solution for resource-constrained research environments. The proposed evaluation framework covers multiple aspects, including sequence quality, computational efficiency, and biological relevance, with comprehensive metrics for assessment. However, the proposal lacks detailed information on how the Mamba model will perform in comparison to other established small-scale models, such as UniRep or TAPE, under identical conditions. Additionally, the scalability of the method for larger, more complex sequences remains unclear, and more discussion on the limitations of the proposed model would be helpful.
rating: 9
confidence: 4 |
I2FbCxyYVS | [Proposal-ML]Protein Sequence Generation Model | [
"pan jiang",
"Zeeshan Zulfiqar",
"Ivan Iazykov"
] | This proposal outlines a novel approach for protein sequence generation, a key area in computational biology focused on generating sequences with specific functional and structural characteristics. Despite recent advances with deep learning models, challenges remain in handling long sequences and capturing biological dynamics. We propose implementing a Mamba architecture that leverages selective state-space updates to achieve efficient protein sequence generation. With linear computational complexity and effective handling of long-range dependencies, this architecture is designed to be more suited to biological dynamics while reducing resource requirements. A comprehensive evaluation on the UniRef50 dataset will demonstrate its potential to deliver competitive performance, providing a viable solution for research environments with limited computational resources. | [
"protein sequence generation",
"Mamba architecture",
"efficient modeling",
"protein language models",
"biological dynamics"
] | https://openreview.net/pdf?id=I2FbCxyYVS | vinzC2A6pr | official_review | 1,731,287,442,831 | I2FbCxyYVS | [
"everyone"
] | [
"~Matteo_Jiahao_Chen1"
] | title: Review of "Protein Sequence Generation Model"
review: This proposal introduces an approach to protein sequence generation using the Mamba architecture. The authors aim to reduce computational complexity while maintaining competitive performance in protein sequence modeling.
## Strengths:
1. **Comprehensive Evaluation:**
The proposal uses a robust evaluation framework .
2. **Practical Implications for Academic Research:**
By focusing on small-scale models, the paper presents a pathway for achieving state-of-the-art results in resource-constrained environments.
## Weaknesses:
1. **Lack of Detailed Comparison with Existing Methods:**
A direct comparison with transformer models and other state-space models would strengthen the argument for Mamba's advantages.
2. **Unclear figures and writing"**:
The caption of the figure could be more detailed, improving the comprehension of the model architecture.
rating: 9
confidence: 4 |
I2FbCxyYVS | [Proposal-ML]Protein Sequence Generation Model | [
"pan jiang",
"Zeeshan Zulfiqar",
"Ivan Iazykov"
] | This proposal outlines a novel approach for protein sequence generation, a key area in computational biology focused on generating sequences with specific functional and structural characteristics. Despite recent advances with deep learning models, challenges remain in handling long sequences and capturing biological dynamics. We propose implementing a Mamba architecture that leverages selective state-space updates to achieve efficient protein sequence generation. With linear computational complexity and effective handling of long-range dependencies, this architecture is designed to be more suited to biological dynamics while reducing resource requirements. A comprehensive evaluation on the UniRef50 dataset will demonstrate its potential to deliver competitive performance, providing a viable solution for research environments with limited computational resources. | [
"protein sequence generation",
"Mamba architecture",
"efficient modeling",
"protein language models",
"biological dynamics"
] | https://openreview.net/pdf?id=I2FbCxyYVS | sYGAJ8oDmn | official_review | 1,731,425,219,119 | I2FbCxyYVS | [
"everyone"
] | [
"~Fei_Long3"
] | title: A Good Proposal But Requires Optimization on Layout
review: **Strengths**:
1. **Innovative Architecture Application:** The proposal demonstrates a significant strength by introducing the Mamba architecture to the field of protein sequence generation, which is a novel approach that could potentially revolutionize how computational biology handles protein modeling, especially given the architecture's ability to handle long sequences and model biological dynamics efficiently.
2. **Addressing Computational Accessibility:** The focus on reducing computational requirements while maintaining competitive performance is a strong point. This work could make advanced protein sequence modeling more accessible to a broader research community.
**Weakness**:
**Figure and Layout Optimization:** While the content of the proposal is strong, the document could benefit from improved figure and layout optimization for enhancing the clarity of these visual elements, which would significantly improve the proposal's impact.
rating: 8
confidence: 4 |
I2FbCxyYVS | [Proposal-ML]Protein Sequence Generation Model | [
"pan jiang",
"Zeeshan Zulfiqar",
"Ivan Iazykov"
] | This proposal outlines a novel approach for protein sequence generation, a key area in computational biology focused on generating sequences with specific functional and structural characteristics. Despite recent advances with deep learning models, challenges remain in handling long sequences and capturing biological dynamics. We propose implementing a Mamba architecture that leverages selective state-space updates to achieve efficient protein sequence generation. With linear computational complexity and effective handling of long-range dependencies, this architecture is designed to be more suited to biological dynamics while reducing resource requirements. A comprehensive evaluation on the UniRef50 dataset will demonstrate its potential to deliver competitive performance, providing a viable solution for research environments with limited computational resources. | [
"protein sequence generation",
"Mamba architecture",
"efficient modeling",
"protein language models",
"biological dynamics"
] | https://openreview.net/pdf?id=I2FbCxyYVS | cbBnRn7DZD | official_review | 1,731,423,940,951 | I2FbCxyYVS | [
"everyone"
] | [
"~liyingxin1"
] | title: Should show more difference between existed methods
review: There are some existed way to generate protein sequence. So maybe it is recommended to clearly articulate the specific objectives and expected outcomes of the study in the introduction to help readers better understand the significance and innovation of the research.
In the methodology section, more technical details could be provided regarding the specific implementation of the Mamba architecture and the selective state update mechanism to help readers better understand the implementation process and technical advantages.
It is recommended to include an experimental section to demonstrate the effectiveness and advantages of the proposed methods in practical applications, particularly in handling long sequences and modeling biological dynamics.
rating: 8
confidence: 4 |
Subsets and Splits