TRIDENT

TRIDENT is a reasoning-focused 4B-parameter language model that improves its own reasoning capability through algorithmic self-improvement, rather than parameter scaling.

The model is built on Qwen3-4B and enhanced using the TRIDENT framework: a combination of GNN-guided Tree-of-Thoughts search, multi-agent reasoning policies, and variance-based self-training.

---

Overview

Traditional large language model training depends on:

• Human-written reasoning traces
• Manually curated preference datasets
• Static fine-tuning pipelines

TRIDENT removes these dependencies.

Instead, the model:

1. Explores multiple reasoning paths
2. Evaluates them using a learned GNN policy
3. Selects high-uncertainty problems automatically
4. Generates its own training supervision
5. Distills improvements back into the model using LoRA

---

model-index:

• name: TRIDENT results:
  • task: type: text-generation dataset: name: GSM8K type: gsm8k split: test metrics:
    • type: accuracy value: 86.58
  • task: type: text-generation dataset: name: MMLU type: mmlu split: test metrics:
    • type: accuracy value: 72.61
  • task: type: text-generation dataset: name: GPQA type: gpqa split: test metrics:
    • type: accuracy value: 42.42
  • task: type: text-generation dataset: name: ARC-Challenge type: arc-challenge split: test metrics:
    • type: accuracy value: 59.0

Core Capabilities

GNN-Guided Tree-of-Thoughts

Reasoning is represented as a directed graph of intermediate states.
A 3-layer Graph Convolutional Network predicts a promise score for each branch, guiding exploration and pruning.

Multi-Agent Reasoning

Four internal agents (Conservative, Exploratory, Balanced, Reflective) vote on reasoning actions to balance exploration and correctness.

Variance-Based Curriculum

Problems are selected for training based on reward variance, targeting examples where the model is inconsistent and learning signal is highest.

Self-Generative Reasoning Loop

No human-annotated reasoning traces are used.
The model autonomously generates, evaluates, and curates its own reasoning data.

Stable Training

A multi-layer reward stabilization mechanism prevents:

• Reward collapse
• Loss explosions
• Infinite failure loops

The architecture is compatible with future GRPO-style reinforcement learning.

---

---

---

Benchmark Results

Accuracy comparison against the base model:

Benchmark	Qwen3-4B	TRIDENT
GSM8K (5-shot)	74.14	86.58
MMLU (5-shot)	47.70	72.61
ARC-C (25-shot)	54.0	59.0
GPQA (0-shot)	28.28	42.42
Winogrande (0-shot)	59.6	67.08
TruthfulQA (0-shot)	54.9	54.7

Highlight:
+14.14 percentage point improvement on GPQA (0-shot).

---

Intended Use

TRIDENT is suitable for:

• Multi-step mathematical reasoning
• Scientific and logical inference
• Hard QA benchmarks
• Planning and hypothesis exploration
• Research on reasoning systems

---

Limitations

• Higher inference-time compute than single-pass models
• Not optimized for low-latency chat
• Best used where reasoning depth matters more than speed

---

Ethical Considerations

• No human-written reasoning traces used
• No preference data collection
• Training relies on verifiable task rewards
• Like all LLMs, may hallucinate outside structured reasoning workflows

---

Paper link

https://www.shivik.in/shivik-labs/trident

Citation

@article{puri2025trident,
  title={TRIDENT: Thought-based Reasoning and Improvement through Deep Exploration of Neuronal Trees},
  author={Puri, Shivansh and Khandelwal, Abhisek and Joshi, Vedant and Yadav, Akash},
  year={2025}
}