Gradient-Adaptive Policy Optimization: Towards Multi-Objective Alignment of Large Language Models

• URL: http://arxiv.org/abs/2507.01915v1
• Date: Wed, 02 Jul 2025 17:25:26 GMT
• Title: Gradient-Adaptive Policy Optimization: Towards Multi-Objective Alignment of Large Language Models
• Authors: Chengao Li, Hanyu Zhang, Yunkun Xu, Hongyan Xue, Xiang Ao, Qing He,
• Abstract summary: Reinforcement Learning from Human Feedback has emerged as a powerful technique for aligning large language models with human preferences.<n>We frame human value alignment as a multi-objective optimization problem, aiming to maximize a set of potentially conflicting objectives.<n>We introduce Gradient-Adaptive Policy Optimization (GAPO), a novel fine-tuning paradigm that employs multiple-gradient descent to align LLMs with diverse preference distributions.
• Score: 19.559468441956714
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reinforcement Learning from Human Feedback (RLHF) has emerged as a powerful technique for aligning large language models (LLMs) with human preferences. However, effectively aligning LLMs with diverse human preferences remains a significant challenge, particularly when they are conflict. To address this issue, we frame human value alignment as a multi-objective optimization problem, aiming to maximize a set of potentially conflicting objectives. We introduce Gradient-Adaptive Policy Optimization (GAPO), a novel fine-tuning paradigm that employs multiple-gradient descent to align LLMs with diverse preference distributions. GAPO adaptively rescales the gradients for each objective to determine an update direction that optimally balances the trade-offs between objectives. Additionally, we introduce P-GAPO, which incorporates user preferences across different objectives and achieves Pareto solutions that better align with the user's specific needs. Our theoretical analysis demonstrates that GAPO converges towards a Pareto optimal solution for multiple objectives. Empirical results on Mistral-7B show that GAPO outperforms current state-of-the-art methods, achieving superior performance in both helpfulness and harmlessness.

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