Efficient Safety Alignment of Large Language Models via Preference Re-ranking and Representation-based Reward Modeling
Year: 2025
Type: conference-paper
Abstract: Reinforcement Learning (RL) algorithms for safety alignment of Large Language Models (LLMs), such as Direct Preference Optimization (DPO), encounter the challenge of distribution shift.Current approaches typically address this issue through online sampling from the target policy, which requires significant computational resources.In this paper, we hypothesize that during off-policy training, while the ranking order of output generated by policy changes, their overall distribution remains relatively stable.This stability allows the conversion of the sampling process from the target policy into a computationally efficient reranking of preference data.Building on this hypothesis, we propose a new framework that leverages the model's intrinsic safety judgment capability to extract reward signals, which are then used to calculate label confidence for preference reordering.Extensive experiments and theoretical analysis demonstrate that the proposed method effectively addresses the distribution shift issue, remarkably enhancing the safety performance while avoiding about 300x computational overheads.
Authors: Deng Qiyuan, Xuefeng Bai, Kehai Chen, Yaowei Wang, Liqiang Nie, Min Zhang
Institutions: Harbin Institute of Technology, Peng Cheng Laboratory
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: G1289754401, G3537643952, G7320519987
OpenAlex ID: W4412889688