SafeDPO: A Simple Approach to Direct Preference Optimization with Enhanced Safety

ICLR 2025 Conference Submission5041 Authors

Keywords: Safety Alignment, LLM Fine-tuning, Preferences, Large Language Models, AI Safety

TL;DR: we present a simple yef effective safety-alignment methods

Abstract: As large language models (LLMs) continue to advance and find applications across a growing number of fields, ensuring the safety of LLMs has become increasingly critical. To address safety concerns, recent studies have proposed integrating safety constraints into reinforcement learning from human feedback (RLHF). However, these approaches tend to be complex and often unstable, as they encompass complicated procedures in RLHF along with additional procedures required by the safety constraints. Inspired by direct preference optimization (DPO), we introduce a new algorithm called \textit{SafeDPO}, which is designed to implicitly optimize the safety alignment objective within a single stage of policy learning. The resulting algorithm can be implemented by introducing only one additional hyperparameter, which aims to further enhance safety, along with minor modifications to the DPO implementation. Consequently, SafeDPO successfully eliminates the necessity of fitting a reward and a cost model, as well as sampling from the language model during fine-tuning, while still enhancing the safety of LLMs. Finally, we demonstrate that SafeDPO achieves competitive performance compared to the current state-of-the-art safety alignment algorithm, both in terms of aligning with human preferences and improving safety.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 5041