Multi-Objective Preference Optimization: Improving Human Alignment of Generative Models

• URL: http://arxiv.org/abs/2505.10892v1
• Date: Fri, 16 May 2025 05:58:26 GMT
• Title: Multi-Objective Preference Optimization: Improving Human Alignment of Generative Models
• Authors: Akhil Agnihotri, Rahul Jain, Deepak Ramachandran, Zheng Wen,
• Abstract summary: We introduce the Multi-Objective Preference Optimization (MOPO) algorithm, which frames alignment as a constrained KL-regularized optimization.<n>Unlike prior work, MOPO operates directly on pairwise preference data, requires no point-wise reward assumption, and avoids prompt-context engineering.
• Score: 15.799929216215672
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Post-training of LLMs with RLHF, and subsequently preference optimization algorithms such as DPO, IPO, etc., made a big difference in improving human alignment. However, all such techniques can only work with a single (human) objective. In practice, human users have multiple objectives, such as helpfulness and harmlessness, and there is no natural way to aggregate them into a single objective. In this paper, we address the multi-objective preference-alignment problem, where a policy must optimize several, potentially conflicting, objectives. We introduce the Multi-Objective Preference Optimization (MOPO) algorithm, which frames alignment as a constrained KL-regularized optimization: the primary objective is maximized while secondary objectives are lower-bounded by tunable safety thresholds. Unlike prior work, MOPO operates directly on pairwise preference data, requires no point-wise reward assumption, and avoids heuristic prompt-context engineering. The method recovers policies on the Pareto front whenever the front is attainable; practically, it reduces to simple closed-form iterative updates suitable for large-scale training. On synthetic benchmarks with diverse canonical preference structures, we show that MOPO approximates the Pareto front. When fine-tuning a 1.3B-parameter language model on real-world human-preference datasets, MOPO attains higher rewards and yields policies that Pareto-dominate baselines; ablation studies confirm optimization stability and robustness to hyperparameters.

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