TinyV: Reducing False Negatives in Verification Improves RL for LLM
  Reasoning
Authors: name: Zhangchen Xu, name: Yuetai Li, name: Fengqing Jiang, name: Bhaskar Ramasubramanian, name: Luyao Niu, name: Bill Yuchen Lin, name: Radha Poovendran

Reinforcement Learning (RL) has become a powerful tool for enhancing the
reasoning abilities of large language models (LLMs) by optimizing their
policies with reward signals. Yet, RL's success relies on the reliability of
rewards, which are provided by verifiers. In this paper, we expose and analyze
a widespread problem--false negatives--where verifiers wrongly reject correct
model outputs. Our in-depth study of the Big-Math-RL-Verified dataset reveals
that over 38% of model-generated responses suffer from false negatives, where
the verifier fails to recognize correct answers. We show, both empirically and
theoretically, that these false negatives severely impair RL training by
depriving the model of informative gradient signals and slowing convergence. To
mitigate this, we propose tinyV, a lightweight LLM-based verifier that augments
existing rule-based methods, which dynamically identifies potential false
negatives and recovers valid responses to produce more accurate reward
estimates. Across multiple math-reasoning benchmarks, integrating TinyV boosts
pass rates by up to 10% and accelerates convergence relative to the baseline.
Our findings highlight the critical importance of addressing verifier false
negatives and offer a practical approach to improve RL-based fine-tuning of
LLMs. Our code is available at https://github.com/uw-nsl/TinyV.