@inproceedings{feng-etal-2026-safer,
    title = "{SAFER}: Advancing Safety Alignment via Efficient Ex-Ante Reasoning",
    author = "Feng, Kehua  and
      Ding, Keyan  and
      Wang, Yuhao  and
      Li, Menghan  and
      Wei, Fanjunduo  and
      Wang, Xinda  and
      Chen, Huajun",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1808/",
    pages = "36268--36289",
    ISBN = "979-8-89176-395-1",
    abstract = "Recent advancements in large language models (LLMs) have accelerated progress toward artificial general intelligence, yet their potential to generate harmful content poses critical safety challenges. Existing alignment methods often struggle to cover diverse safety scenarios and remain vulnerable to adversarial attacks. In this work, we propose **SAFER**, a framework for **S**afety **A**lignment via e**F**ficient **E**x-Ante **R**easoning. Our approach instantiates structured Ex-Ante reasoning through initial assessment, rule verification, and path calibration, and embeds predefined safety rules to provide transparent and verifiable safety judgments. Specifically, our approach consists of two training stages: (1) supervised fine-tuning with synthetic traces to teach the multi-stage Ex-Ante reasoning, and (2) step-level reasoning preference optimization to jointly enhance safety, utility, and efficiency. Experiments on multiple open-source LLMs demonstrate that SAFER significantly enhances safety performance while maintaining helpfulness and response efficiency."
}