First Token Matters: Understanding Safety Collapse in Large Reasoning Models

2026-09-16

AI Summary

研究通过分析大型推理模型在处理有害查询时的安全性问题，提出了一种名为SafeToken的轻量级方法，在推理开始时注入安全锚点以提高安全性。

Abstract

Large Reasoning Models (LRMs) exhibit strong problem-solving abilities, yet their safety alignment often degrades when handling harmful queries. Existing approaches to improving safety largely rely on additional training or preference optimization, while offering limited understanding of the internal mechanisms behind safety failures. In this work, we investigate this failure through a token-level positional analysis of refusal dynamics and identify a localized vulnerability at the onset of reasoning, which we term Onset Refusal Collapse (ORC). We find that the refusal-related signal of LRMs drops sharply at the first generated token under harmful queries, which is associated with unsafe response generation. Motivated by this finding, we propose SafeToken, a lightweight inference-time intervention that injects a learned continuous safety anchor precisely at reasoning onset. Despite updating only a single token embedding, SafeToken effectively mitigates ORC, improves safety on harmful-query benchmarks, and largely preserves reasoning utility. These results suggest that safety failures in LRMs can arise from a transient breakdown at the critical transition from understanding to generation.

Problem

• Large Reasoning Models
• Safety Alignment
• Harmful Queries
• Onset Refusal Collapse
• Internal Mechanisms

Innovation

• Onset Refusal Collapse (ORC)
• SafeToken
• token-level positional analysis