arXiv:2602.13562v2 Announce Type: replace-cross Abstract: While reasoning models have achieved remarkable success in complex reasoning tasks, their increasing power necessitates stringent safety measures. For safety alignment, the core challenge lies in the inherent trade-off between safety and utility. However, prevailing alignment strategies typically construct CoT training data with explicit safety rules via context distillation. This approach inadvertently limits reasoning capabilities by creating a rigid association between rule memorization and refusal. To mitigate the safety-utility tra
The increasing power of large language models necessitates ongoing research into safety mechanisms, and the publication of this paper by arXiv cs.AI indicates a current focus on refining LLM alignment techniques.
Achieving effective safety alignment without compromising the utility and reasoning capabilities of LLMs is critical for their widespread and responsible deployment across various sectors.
This research proposes a method to mitigate the safety-utility trade-off in LLM alignment, moving away from rigid rule memorization towards more adaptive and context-aware safety mechanisms.
- · LLM developers
- · AI safety researchers
- · Sectors adopting LLMs
- · Rigid alignment strategies
- · Users encountering overly cautious LLMs
Adaptive safety mechanisms could lead to more capable and less constrained large language models.
Improved LLM performance and trustworthiness may accelerate their integration into critical applications and services.
The development of highly adaptive and context-aware safety could pave the way for genuinely autonomous and robust AI agents.
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Read at arXiv cs.AI