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cs.LG2026
Learning from Evolving Errors: Adaptive Iterative Repair for On-Policy Distillation
Rui Li, Liyang He, Zheng Zhang +3
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 reward…
cs.LG2026
DuoOPD: Learning from Joint Teacher-Student Outcomes for Multi-Task On-Policy Distillation
Ao Yu, Weibo Gao, Heng Zhou +6
On-policy distillation (OPD) trains a student on its own responses with token-level feedback from a stronger teacher, yet the teacher can fail on questions the student already answ…