Don't Waste Mistakes: Leveraging Negative RL-Groups via Confidence Reweighting

• URL: http://arxiv.org/abs/2510.08696v1
• Date: Thu, 09 Oct 2025 18:01:44 GMT
• Title: Don't Waste Mistakes: Leveraging Negative RL-Groups via Confidence Reweighting
• Authors: Yunzhen Feng, Parag Jain, Anthony Hartshorn, Yaqi Duan, Julia Kempe,
• Abstract summary: Reinforcement learning with verifiable rewards (RLVR) has become a standard recipe for improving large language models.<n>We show that negative groups can be leveraged without extra supervision.
• Score: 24.822152032771736
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a standard recipe for improving large language models (LLMs) on reasoning tasks, with Group Relative Policy Optimization (GRPO) widely used in practice. Yet GRPO wastes substantial compute on negative groups: groups in which no sampled response is correct yield zero advantage and thus no gradient. We ask whether negative groups can be leveraged without extra supervision. Starting from a maximum-likelihood (MLE) objective in reward modeling, we show that the MLE gradient is equivalent to a policy gradient for a modified value function. This value function adds a confidence-weighted penalty on incorrect responses, imposing larger penalties on more confident mistakes. We refer to this as \textbf{L}ikelihood \textbf{E}stimation with \textbf{N}egative \textbf{S}amples (\textbf{LENS}). LENS modifies GRPO to assign non-zero, confidence-dependent rewards to incorrect generations, making negative groups informative and converting previously wasted samples into useful gradient updates. On the MATH benchmark with Llama-3.1-8B and Qwen-2.5-3B, the proposed variant consistently outperforms GRPO baseline, with significant gains on harder items. These results demonstrate a principled and practical way to "rescue" negative groups, improving efficiency and performance in RLVR.

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