Multi-Objective Alignment (MOA) aims to align LLMs' responses with multiple human preference objectives, with Direct Preference Optimization (DPO) emerging as a prominent approach. However, we find that DPO-based MOA approaches suffer from widespread preference conflicts in the data, where different objectives favor different responses. This results in conflicting optimization directions, hindering the optimization on the Pareto Front. To address this, we propose to construct Pareto-optimal responses to resolve preference conflicts. To efficiently obtain and utilize such responses, we propose a self-improving DPO framework that enables LLMs to self-generate and select Pareto-optimal responses for self-supervised preference alignment. Extensive experiments on two datasets demonstrate the superior Pareto Front achieved by our framework compared to various baselines. Code is available at https://github.com/zyttt-coder/SIPO.
@article{arxiv.2502.14354,
title = {Self-Improvement Towards Pareto Optimality: Mitigating Preference Conflicts in Multi-Objective Alignment},
author = {Moxin Li and Yuantao Zhang and Wenjie Wang and Wentao Shi and Zhuo Liu and Fuli Feng and Tat-Seng Chua},
journal= {arXiv preprint arXiv:2502.14354},
year = {2025}
}