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Dr. OPD: Learning What to Follow for Optimal On-Policy Distillation of Large Language Models
On-policy distillation (OPD) trains a student on its own generated responses using dense, token-level supervision from a stronger teacher. Vanilla OPD treats all teacher signals equally, assuming that the teacher's supervision is equally important for every token. However, teacher signals at differe
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