2025/04/03 by Kehua Feng, Feng, Kehua, Keyan Ding +12 · 2 citations
Computer Science · Decision Sciences · Engineering · #Computer science #Economics #Ex-ante #Microeconomics #Preference #Risk and Safety Analysis #Safety Systems Engineering in Autonomy #Software Reliability and Analysis Research
paper · pdf · doi:10.48550/arxiv.2504.02725
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/04/03 · openalex created_date 2025/05/13 · openalex updated_date 2026/08/05
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 Safety Alignment via eFficient Ex-Ante Reasoning. 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.