Leveraging Error Diversity in Group Rollouts for Reinforcement Learning
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 training success, with problems eliciting diverse wrong answers benefiting substantially more from RLVR than those producing homogeneous failures. Motivated by this observation, we propose Error Diversity Advantage Shaping (EDAS), a lightweight, algorithm-agnostic technique that modulates th...
| Authors | Wenpu Liu, Yuqi Xu, Weichu Xie et al. |
|---|---|
| Primary category | cs.LG |
| Published | 17 May 2026 |
| Last updated at source | 5 June 2026 |
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