ActionOPD: Distilling Decisions, Not Tokens
This work introduces ActionOPD, an action-level on-policy distillation method motivated by maximizing expected teacher-action likelihood at student-encountered states, and establishes ActionOPD as a promising approach to agent distillation.
Hui-Ning Yuan, Xiang-Min Yi, Ze-Lai Xu et al.
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