Title: Mitigating Think-Answer Mismatch in LLM Reasoning Through Noise-Aware Advantage Reweighting
Primary area: reinforcement learning
Keywords: Reasoning Model, Large Language Model, Group-Relative Policy Optimization, Noise Model, Reinforcement Learning

Abstract: Group-Relative Policy Optimization (GRPO) is a key technique for training large reasoning models, yet it suffers from a critical vulnerability: the Think-Answer Mismatch, where noisy reward signals corrupt the learning process. This problem is most severe in unbalanced response groups, paradoxically degrading the signal precisely when it should be most informative. To address this challenge, we propose Stable Group-Relative Policy Optimization (S-GRPO), a principled enhancement that derives optimal, noise-aware advantage weights to stabilize training. Our comprehensive experiments on mathematical reasoning benchmarks demonstrate S-GRPO's effectiveness and robustness. On various models, S-GRPO significantly outperforms DR. GRPO, achieving performance gains of +2.5\% on Qwen-Math-7B-Base, +2.2\% on Llama-3.2-3B-Base, and +2.4\% on Qwen-Math-1.5B-Instruct. Most critically, while standard GRPO fails to learn under 20\% synthetic reward noise, S-GRPO maintains stable learning progress. These results highlight S-GRPO's potential for more robust and effective training of large-scale reasoning models.