Pareto Multi-Objective Alignment for Language Models

📄 arXiv: 2508.07768v1 📥 PDF

作者: Qiang He, Setareh Maghsudi

分类: cs.LG, cs.AI, cs.CL

发布日期: 2025-08-11

备注: Accepted at ECML/PKDD 2025

💡 一句话要点

提出Pareto多目标对齐以解决语言模型的多重目标优化问题

🎯 匹配领域: 支柱二：RL算法与架构 (RL & Architecture) 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 多目标对齐 语言模型 强化学习 凸优化 人类反馈 算法效率 模型适应性

📋 核心要点

  1. 现有的对齐方法主要基于单一奖励函数，导致语言模型在多目标优化中表现出僵化的行为，无法适应复杂的人类偏好。
  2. 本文提出的Pareto多目标对齐（PAMA）算法，通过将多目标RLHF转化为凸优化问题，显著提高了优化效率和可扩展性。
  3. 实验结果显示，PAMA在125M到7B参数的语言模型中均表现出色，优化复杂度降低至O(n)，验证了其理论和实际应用的有效性。

📝 摘要（中文）

大型语言模型（LLMs）在实际应用中需要平衡多种常常相互冲突的目标，如信息量与简洁性、帮助性与创造性。然而，现有的对齐方法主要基于强化学习与人类反馈（RLHF），优化单一奖励函数，导致模型行为僵化，无法捕捉人类偏好的复杂性和多样性。为了解决这一问题，本文提出了Pareto多目标对齐（PAMA），一种专门为LLMs设计的高效算法。PAMA将多目标RLHF转化为具有闭式解的凸优化，显著提高了可扩展性，复杂度降低至O(n)，使得优化过程在毫秒级内完成。实验结果表明，PAMA在多种参数规模的语言模型中展现了强大的多目标对齐能力，验证了其理论优势。

🔬 方法详解

问题定义：本文旨在解决大型语言模型在多目标对齐中的局限性，现有方法因优化单一奖励函数而导致模型行为僵化，无法满足复杂的人类偏好。

核心思路：PAMA通过将多目标强化学习与人类反馈（RLHF）转化为凸优化问题，提供了一种高效的解决方案，避免了传统多目标优化方法的高复杂度。

技术框架：PAMA的整体架构包括目标函数的定义、优化算法的设计以及收敛性分析，主要模块包括目标选择、权重调整和优化执行。

关键创新：PAMA的核心创新在于将多目标优化的复杂度从O(n^2*d)降低到O(n)，使得在大规模模型中实现多目标对齐成为可能。

关键设计：PAMA采用了闭式解的凸优化技术，设计了适应性权重调整机制，确保在多目标优化中各目标之间的平衡，同时保证收敛性和效率。

📊 实验亮点

实验结果表明，PAMA在不同规模的语言模型中均表现出色，优化复杂度显著降低至O(n)，在125M到7B参数的模型中均能在毫秒级内完成多目标对齐，验证了其理论优势和实际应用潜力。

🎯 应用场景

该研究的潜在应用领域包括智能助手、内容生成、教育技术等，能够帮助语言模型更好地适应用户的多样化需求，提升人机交互的自然性和有效性。未来，PAMA有望在更多实际场景中推广应用，推动AI系统的智能化和人性化发展。

📄 摘要（原文）

Large language models (LLMs) are increasingly deployed in real-world applications that require careful balancing of multiple, often conflicting, objectives, such as informativeness versus conciseness, or helpfulness versus creativity. However, current alignment methods, primarily based on RLHF, optimize LLMs toward a single reward function, resulting in rigid behavior that fails to capture the complexity and diversity of human preferences. This limitation hinders the adaptability of LLMs to practical scenarios, making multi-objective alignment (MOA) a critical yet underexplored area. To bridge this gap, we propose Pareto Multi-Objective Alignment (PAMA), a principled and computationally efficient algorithm designed explicitly for MOA in LLMs. In contrast to computationally prohibitive multi-objective optimization (MOO) methods, PAMA transforms multi-objective RLHF into a convex optimization with a closed-form solution, significantly enhancing scalability. Traditional MOO approaches suffer from prohibitive O(n^2*d) complexity, where d represents the number of model parameters, typically in the billions for LLMs, rendering direct optimization infeasible. PAMA reduces this complexity to O(n) where n is the number of objectives, enabling optimization to be completed within milliseconds. We provide theoretical guarantees that PAMA converges to a Pareto stationary point, where no objective can be improved without degrading at least one other. Extensive experiments across language models ranging from 125M to 7B parameters demonstrate PAMA's robust and effective MOA capabilities, aligning with its theoretical advantages. PAMA provides a highly efficient solution to the MOA problem that was previously considered intractable, offering a practical and theoretically grounded approach to aligning LLMs with diverse human values, paving the way for versatile and adaptable real-world AI deployments.