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Spotlight Poster

One-Shot Safety Alignment for Large Language Models via Optimal Dualization

Xinmeng Huang · Shuo Li · Edgar Dobriban · Osbert Bastani · Hamed Hassani · Dongsheng Ding

East Exhibit Hall A-C #2708
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Fri 13 Dec 4:30 p.m. PST — 7:30 p.m. PST

Abstract:

The growing safety concerns surrounding large language models raise an urgent need to align them with diverse human preferences to simultaneously enhance their helpfulness and safety. A promising approach is to enforce safety constraints through Reinforcement Learning from Human Feedback (RLHF). For such constrained RLHF, typical Lagrangian-based primal-dual policy optimization methods are computationally expensive and often unstable. This paper presents a perspective of dualization that reduces constrained alignment to an equivalent unconstrained alignment problem. We do so by pre-optimizing a smooth and convex dual function that has a closed form. This shortcut eliminates the need for cumbersome primal-dual policy iterations, greatly reducing the computational burden and improving training stability. Our strategy leads to two practical algorithms in model-based and preference-based settings (MoCAN and PeCAN, respectively). A broad range of experiments demonstrate the effectiveness and merits of our algorithms.

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