Title: A Systematic Investigation of RL-Jailbreaking in LLMs

Abstract: As generative models transition from simple next-token predictors to autonomous engines capable of managing complex systems, rigorous safety hardening has become essential. Adversarial jailbreaking, which involves strategically manipulating models to generate harmful content, continues to pose a significant risk to safe deployment. Although Reinforcement Learning (RL) approaches jailbreaking as a multi-step attack utilizing sequential optimization, the underlying mechanisms explaining the framework's effectiveness are not yet fully understood. To address this knowledge gap, we offer the first systematic decomposition of RL-based jailbreaking. We break down the framework into its core components: problem formalization (including reward functions, action spaces, and episode lengths) and algorithmic strategies (such as the RL algorithm, training data, and reward shaping) to pinpoint the structural factors driving adversarial success. Our findings indicate that the RL-jailbreaker successfully breached all targeted models and their associated safeguards. This pioneering analysis demonstrates that environment formalization—particularly the use of dense rewards and prolonged episode lengths—is the main catalyst for jailbreaking success. By providing insights into these dynamics, this work offers a pathway to enhance the efficiency of RL-jailbreakers and, consequently, to strengthen generative models against such attacks.

Source: arXiv Generated at: 2026-06-04 00:00:00 UTC