arXiv:2606.04168v1 Announce Type: new Abstract: Safety alignment in large language models (LLMs) is fragile in part because it is often shallow: fine-tuning mainly reshapes the model's behavior near the first few output tokens. We argue that this phenomenon can be understood through autoregressive consistency, the tendency of next-token prediction to preserve and extend the current response trajectory consistently. By analyzing the learning dynamics of safety alignment, we show that autoregressive consistency can concentrate alignment updates on early tokens, offering a mechanistic explanation
The accelerating deployment and reliance on large language models (LLMs) make understanding and improving their safety alignment a critical, current research focus.
This research provides a mechanistic explanation for LLM safety failures, highlighting a fundamental limitation in current alignment techniques and suggesting areas for future improvement.
The understanding of LLM safety alignment shifts from 'fragile' to a 'mechanistically explained fragility' due to autoregressive consistency, potentially leading to more robust alignment strategies.
- · AI safety researchers
- · Developers of LLM alignment techniques
- · Users of safer LLMs
- · Organizations relying on shallow LLM safety
- · Legacy LLM alignment methods
Research efforts will likely pivot to address autoregressive consistency in LLM safety alignment, possibly focusing on multi-turn or contextual alignment.
New alignment methodologies emerge that are less susceptible to 'early token' focusing, leading to more genuinely aligned and trustworthy LLMs.
The increased robustness of LLM safety measures could accelerate their integration into sensitive applications, expanding the AI agent paradigm with greater confidence.
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Read at arXiv cs.LG