Post-training at the edge of detectability: a game-theoretic approach to fine-tuning
Read the original at arxiv.org→arXiv:2607.26358v1 Announce Type: new Abstract: Reinforcement learning (RL) fine-tuning is widely used in language model training to improve model performance on a target task while limiting drift from a reference...
Original headline: "Post-Training at the Edge of Detectability: A Game-Theoretic Approach to Fine-Tuning"
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- Jul 30, 04:00 UTC arXiv cs.LG lead source Post-Training at the Edge of Detectability: A Game-Theoretic Approach to Fine-Tuning