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arXiv AI By Yafeng Tang, Hao Li, Hongsheng Yu, Qiang Fu

ReTeach: Building a Self-Teacher through Multi-Round Reflection and Retry

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ReTeach is a reflective self‑distillation framework that builds a self‑teacher through multiple rounds of reflection and retry, relying only on self‑generated attempts and outcome‑level verification. Starting from an unsuccessful student rollout, the teacher alternates explicit reflection with new attempts until success or a retry limit is reached, without needing reference answers, external feedback, or cross‑example memory. The method uses outcome‑aware weighting to distinguish correct, reflection‑corrected, and unresolved examples, and on‑policy distillation aligns the student’s token‑level predictions with the teacher’s context‑conditioned distributions, achieving a 1.39‑point accuracy gain over GRPO across six reasoning benchmarks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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