Provably Mitigating Overoptimization in RLHF: Y our SFT Loss is Implicitly an Adversarial Regularizer

Oct-10-2025, 22:05:18 GMT–Neural Information Processing Systems 

Then it fine-tunes the LLM to maximize the learned reward using RL techniques.