arXiv:2606.31748v1 Announce Type: new Abstract: Safety training on language models often induces over-refusal: improved safety on harmful prompts at the cost of increased refusal on harmless ones. Though this trade-off can be mitigated by training models with reinforcement learning (RL) to reason before answering, it does not remove the underlying problem that reasoning can often be a "rubber stamp" for a predetermined response. In this paper, we address the safety-refusal trade-off by rethinking how models are trained to reason about safety. Our key insight is that unsafe reasoning can itself
The paper addresses a critical, known bottleneck in current LLM safety training methods, which becomes more urgent as LLM deployment expands to sensitive applications.
Improving LLM safety without inducing over-refusal is crucial for commercial viability and user acceptance, directly impacting the effective deployment of AI.
New methodologies for training LLMs could lead to more nuanced and less restrictive AI responses, opening up new use cases and reducing current operational frustrations.
- · AI developers
- · LLM users
- · AI-driven product companies
- · Companies relying on over-cautious AI if competitors adopt better safety trainin
LLMs will become more capable of engaging with complex, nuanced requests without incorrectly refusing harmless queries.
Public trust and adoption of advanced AI systems could increase, accelerating integration into various industries.
The definition and implementation of 'AI safety' could evolve, moving beyond simple refusal to more context-aware reasoning.
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Read at arXiv cs.LG