Revisiting Reinforcement Learning with Verifiable Rewards from a Contrastive Perspective
Quick summary
arXiv:2605.12969v4 Announce Type: replace-cross Abstract: Group Relative Policy Optimization (GRPO) is one of the most widely adopted RLVR algorithms for post-training large language models on reasoning tasks. We first show that GRPO admits an equivalent discriminative reformulation, in which policy optimization maximizes the expected score gap between verified positive and negative rollouts. This reformulation reveals two objective-level limitations: likelihood-misaligned surrogate scores, in which clipped ratio-based scores are optimized rather than the sequence likelihoods that govern gener
Key takeaways
- arXiv:2605.12969v4 Announce Type: replace-cross Abstract: Group Relative Policy Optimization (GRPO) is one of the most widely adopted RLVR algorithms for post-training large language models on reasoning tasks.
- We first show that GRPO admits an equivalent discriminative reformulation, in which policy optimization maximizes the expected score gap between verified positive and negative rollouts.
- This reformulation reveals two objective-level limitations: likelihood-misaligned surrogate scores, in which clipped ratio-based scores are optimized rather than the sequence likelihoods that govern gener
Why it matters
The significance is not only the legal text but how it changes product design. Decisions around “Revisiting Reinforcement Learning with Verifiable Rewards from a Contrastive Perspective” may reshape data collection, model training, output accountability and market access.
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