[2604.02652] Generalization Limits of Reinforcement Learning Alignment

Computer Science > Machine Learning arXiv:2604.02652 (cs) [Submitted on 3 Apr 2026] Title:Generalization Limits of Reinforcement Learning Alignment Authors:Haruhi Shida, Koo Imai, Keigo Kansa View a PDF of the paper titled Generalization Limits of Reinforcement Learning Alignment, by Haruhi Shida and 2 other authors View PDF HTML (experimental) Abstract:The safety of large language models (LLMs) relies on alignment techniques such as reinforcement learning from human feedback (RLHF). However, recent theoretical analyses suggest that reinforcement learning-based training does not acquire new capabilities but merely redistributes the utilization probabilities of existing ones. In this study, we propose ``compound jailbreaks'' targeting OpenAI gpt-oss-20b, which exploit the generalization failures of alignment. This approach combines multiple attack techniques -- each individually defended against -- to saturate the instruction hierarchy maintenance process. Our evaluation shows that the attack success rate (ASR) increased from 14.3\% with individual methods to 71.4\% with the combined approach. These results provide empirical evidence for the hypothesis that safety training does not generalize as broadly as model capabilities, highlighting the need for multifaceted safety evaluations using compound attack scenarios. Comments: Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2604.02652 [cs.LG]   (or arXiv:2604.02652v1 [cs.LG] for this version...