A Systematic Investigation of RL-Jailbreaking in LLMs 事件

A Systematic Investigation of RL-Jailbreaking in LLMs arXiv:2605.07032v2 Announce Type: replace-cross Abstract: The evolution of generative models from next-token predictors to autonomous engines of complex systems necessitates rigorous safety hardening. Adversarial jailbreaking, the strategic manipulation of models to elicit harmful output, remains a primary threat to safe deployment. While Reinforcement Learning (RL) frames jailbreaking as a multi-step attack through sequential optimization,