Author

Rui Li

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#machine learning Preprint Oct 2026

Learning from Evolving Errors: Adaptive Iterative Repair for On-Policy Distillation

On-policy self-distillation (OPSD) supplies dense token-level feedback on trajectories sampled from the student's own policy, a richer training signal than the outcome-level rewards of reinforcement learning. This feedback comes from a teacher conditioned on a full reference solution unavailable to the student. The ref...

Rui Li, Li-Yang He, Zheng Zhang et al. · 0 citations

#machine learning Preprint Sep 2026

DuoOPD: Learning from Joint Teacher-Student Outcomes for Multi-Task On-Policy Distillation

DuoOPD is introduced, in which the student's outcome sets the direction of feedback and the joint teacher-student outcome decides how the teacher supports it: when only the teacher succeeds, its verified answer becomes context for scoring the student's failed response.

Ao Yu, Weibo Gao, Heng Zhou et al. · 0 citations