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Does On-Policy Distillation Really Distill? From Noisy Teacher to Self-Improvement

Ding, Yi et al.
arXiv (All) · Papers
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computation-and-languagemachine-learning
On-policy distillation (OPD) offers dense token-level supervision as an alternative to the sparse outcome-level advantages of reinforcement learning with verifiable rewards (RLVR). However, the teacher scores student-generated trajectories that are inherently off-policy for it, so the reliability of
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