When Right Meets Wrong: Bilateral Context Conditioning with Reward-Confidence Correction for GRPO
Group Relative Policy Optimization (GRPO) has emerged as an effective method for training reasoning models. While it computes advantages based on group mean, GRPO treats each output as an independent sample during the optimization and overlooks a vital structural signal: the natural contrast between correct and incorrect solutions within the same group, thus ignoring the rich, comparative data that could be leveraged by explicitly pitting successful reasoning traces against failed ones. To capitalize on this, we present a contrastive reformulation of GRPO, showing that the GRPO objective implicitly maximizes the margin between the policy ratios of correct and incorrect samples. Building o...
| Authors | Yu Li, Tian Lan, Zhengling Qi |
|---|---|
| Primary category | cs.AI |
| Published | 13 March 2026 |
| Last updated at source | 13 March 2026 |
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