Reasoning Structure Matters for Safety Alignment of Reasoning Models

ACL ARR 2026 January Submission8751 Authors

Keywords: Large reasoning models, safety alignment

Abstract: Large reasoning models (LRMs) achieve strong performance on complex reasoning tasks but often generate harmful responses to malicious user queries. This paper investigates the underlying cause of these safety risks and shows that the issue lies in the reasoning structure itself. Based on this insight, we claim that effective safety alignment can be achieved by altering the reasoning structure. We propose AltTrain, a simple yet effective post-training method that explicitly alters the reasoning structure of LRMs. AltTrain is both practical and generalizable, requiring no complex reinforcement learning (RL) training or reward design—only supervised fine-tuning (SFT) with a lightweight 1K training examples. Experiments across LRM backbones and model sizes demon strate strong safety alignment, along with robust generalization across reasoning, QA, summarization, and multilingual setting.

Paper Type: Long

Research Area: Safety and Alignment in LLMs

Research Area Keywords: Language Modeling, AI / LLM Agents, Efficient/Low-Resource Methods for NLP

Contribution Types: NLP engineering experiment, Approaches low compute settings-efficiency, Publicly available software and/or pre-trained models, Data resources, Data analysis

Languages Studied: English, Chinese

Submission Number: 8751