arXiv (OAI Expanded)
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Unifying ICL, SFT, KL-Regularized RL Through a Bayesian Lens
Supervised fine-tuning (SFT), few-shot in-context learning (ICL), KL-regularized RLHF/RLVR, and on-policy distillation are usually treated as distinct post-training paradigms. We develop a unified Bayesian perspective in which each is an instance of a two-step template: construct a (generalized) Bay
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