AI RESEARCH
Leveraging Error Diversity in Group Rollouts for Reinforcement Learning
arXiv CS.LG
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ArXi:2605.17333v1 Announce Type: new Reinforcement Learning from Verifiable Rewards (RLVR) typically samples multiple responses per prompt and assigns binary rewards based on individual correctness, yet the collective structure of the group output, specifically the distribution of errors, is largely discarded. We identify this as a missed opportunity: empirical analysis reveals that error diversity within a group is a strong predictor of