@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T SAFER: Advancing Safety Alignment via Efficient Ex-Ante Reasoning
%A Feng, Kehua
%A Ding, Keyan
%A Wang, Yuhao
%A Li, Menghan
%A Wei, Fanjunduo
%A Wang, Xinda
%A Chen, Huajun
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F feng-etal-2026-safer
%X 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.
%U https://aclanthology.org/2026.findings-acl.1808/
%P 36268-36289
Markdown (Informal)
[SAFER: Advancing Safety Alignment via Efficient Ex-Ante Reasoning](https://aclanthology.org/2026.findings-acl.1808/) (Feng et al., Findings 2026)
ACL
- Kehua Feng, Keyan Ding, Yuhao Wang, Menghan Li, Fanjunduo Wei, Xinda Wang, and Huajun Chen. 2026. SAFER: Advancing Safety Alignment via Efficient Ex-Ante Reasoning. In Findings of the Association for Computational Linguistics: ACL 2026, pages 36268–36289, San Diego, California, United States. Association for Computational Linguistics.