Suan: Rectifying Direct Preference Safety Alignment in Large Language Models

Abstract:Integrating robust safety guardrails into Large Language Models (LLMs) is essential for delivering helpful yet harmless responses. While proprietary systems exhibit reliable safety controls, their underlying methodologies and trade-offs remain largely undisclosed. Achieving comparable security in open-weight models remains a persistent challenge, as post-trained variants frequently suffer from over-refusal and degraded general quality. To overcome these drawbacks, we introduce Suan, a novel preference optimization algorithm. Unlike existing methods, we formulate the optimization objective directly at the gradient level, bypassing the standard variational derivation. As a result, we obtain more interpretable and robust training dynamics. Extensive evaluations across a diverse suite of competitive baselines and benchmarks demonstrate that Suan achieves superior safety alignment while fully preserving response utility.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2609.08634 [cs.LG]

(or arXiv:2609.08634v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.08634 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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