AI RESEARCH
Self-Supervised On-Policy Distillation for Reasoning Language Models
arXiv CS.LG
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ArXi:2605.17497v1 Announce Type: new GRPO-style RLVR trains reasoning models from multiple on-policy attempts per prompt, but typically uses these attempts only through terminal rewards. We show that a mixed group contains a richer process signal: a correct completion is a self-generated witness of how the current policy can solve the problem, while a wrong completion provides on-policy prefixes where the policy needs correction. We