@inproceedings{in-etal-2026-reasoning,
    title = "Reasoning Structure Matters for Safety Alignment of Reasoning Models",
    author = "In, Yeonjun  and
      Kim, Wonjoong  and
      Park, Sangwu  and
      Park, Chanyoung",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.240/",
    pages = "5302--5318",
    ISBN = "979-8-89176-390-6",
    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."
}