Zhengyue Zhao, Liwei Jiang, Yejin Choi, Chaowei Xiao1 · 2026 · arXiv
DOI: 10.48550/arXiv.2610.26809 · open-access PDF
Safety alignment for large language models (LLMs) is primarily performed during post-training, while recent work has also begun incorporating safety data into pre-training. Yet safety can degrade when later training no longer includes safety data, and gains in safety often come at the cost of general capability. This tension suggests that safety and general reasoning should not be treated as separate training goals. We instead view safety as an LLM capability that can be learned through a reasoning structure shared with general tasks. Both require the model to understand intent or task goals,
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