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Post-Training at the Edge of Detectability: A Game-Theoretic Approach to Fine-Tuning
Reinforcement learning (RL) fine-tuning is widely used in language model training to improve performance on a target task while limiting drift from a reference policy. A standard way to balance this trade-off is via a KL-regularized RL objective, although this formulation does not by itself provide
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