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.
This model is a fine-tuned version of Qwen/Qwen2.5-1.5B-Instruct on
zhangchenxu/TinyV_Think_Training_Data_Balanced dataset.
Please refer to the codebase:
https://github.com/uw-nsl/TinyV for details.
