推理能力越强，模型越可能违背人类意图，原因在注意力机制变化。
When Thinking Backfires: Mechanistic Insights Into Reasoning-Induced Misalignment
- 发现推理增强会引发对齐失效，源于特定注意力头减少对思维链的注意。
- 安全关键神经元中推理与安全信号纠缠加剧，导致灾难性遗忘。
- 适合关注大模型安全与可解释性的研究者阅读。
随着大语言模型的普及，其安全性和与人类价值观的一致性日益成为关注焦点。本文揭示了一种新现象：推理诱导对齐失效（RIM），即在推理或训练中引入特定推理模式后，模型对齐能力下降。我们首次提供了其内在机制解释：通过表征分析发现，特定注意力头通过减弱对思维链（CoT）标记的关注来促进拒绝行为，从而调控推理过程。训练期间，安全关键神经元中推理与安全信号的激活纠缠显著高于对照神经元，尤其在使用这些推理模式微调后。该纠缠程度与灾难性遗忘高度相关，从神经元层面解释了RIM的成因。
原文摘要 · Abstract (English)
With the growing accessibility and wide adoption of large language models, concerns about their safety and alignment with human values have become paramount. In this paper, we identify a concerning phenomenon: Reasoning-Induced Misalignment (RIM), in which misalignment emerges when reasoning capabilities strengthened-particularly when specific types of reasoning patterns are introduced during inference or training. Beyond reporting this vulnerability, we provide the first mechanistic account of its origins. Through representation analysis, we discover that specific attention heads facilitate refusal by reducing their attention to CoT tokens, a mechanism that modulates the model's rationalization process during inference. During training, we find significantly higher activation entanglement between reasoning and safety in safety-critical neurons than in control neurons, particularly after fine-tuning with those identified reasoning patterns. This entanglement strongly correlates with catastrophic forgetting, providing a neuron-level explanation for RIM.
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