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A unified view is developed in which the log-probability ratio between a proxy expert and a proxy reference defines an implicit reward for KL-regularized policy optimization, which enables any-to-any transfer across models that differ in scale, training stage or generation.
REOPD combines a token-level compatibility weight with a batch-level adaptive budget, yielding a token-wise coefficient $\lambda_{b,t}=1+\gamma_b q_t$ that preserves teacher alignment while selectively extrapolating along reliable teacher-reference directions, demonstrating effective fine-grained reliability adaptation...
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