zhangchenxu/TinyV-Qwen3-1.7B-Think

TinyV is a reward system for efficient RL post-training that detects false negatives in current rule-based verifiers and provides more accurate reward signals via a small LLM during RL training. Experiments show that TinyV incurs only 6% additional computational cost while significantly increasing both RL efficiency and final model performance.

• 📄 Technical Report - Including false negative analysis and theotical insights behind TinyV
• 💾 Github Repo - Access the complete pipeline for more efficient RL training via TinyV
• 🤗 HF Collection - Training Data, Benchmarks, and Model Artifact

This model is a fine-tuned version of Qwen/Qwen3-1.7B on zhangchenxu/TinyV_Think_Training_Data_Qwen3_Balanced dataset.

Overview

How to use it?

Please refer to the codebase: https://github.com/uw-nsl/TinyV for details.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 1e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• gradient_accumulation_steps: 8
• total_train_batch_size: 512
• total_eval_batch_size: 64
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 2.0

Framework versions

• Transformers 4.52.4
• Pytorch 2.7.1+cu126
• Datasets 3.6.0
• Tokenizers 0.21.1