让多个AI助手协作更默契，提升复杂任务完成率。
Bridging the Capability Gap: Joint Alignment Tuning for Harmonizing LLM-based Multi-Agent Systems
- 通过交替优化规划与执行两类智能体，实现协同对齐。
- 在6个基准测试中，对未见任务提升4.4%准确率。
- 适合研究多智能体系统协作或提升AI团队效率者。
大型语言模型的发展使得通过分工协作的多智能体系统能够解决复杂任务，例如由规划智能体生成子目标，由具身智能体执行工具操作。现有方法通常独立微调各智能体，导致能力差距大、协作不佳。为此，本文提出MOAT框架，通过迭代对齐提升智能体协作能力。MOAT包含两个关键阶段：(1) 规划智能体对齐，优化规划智能体生成更利于执行智能体理解的子目标序列；(2) 具身智能体改进，利用智能体自身生成的多样化子目标-动作对进行微调，增强泛化能力。理论分析表明，该过程单调不减且逐步收敛。在六个基准测试上，MOAT显著优于现有方法，在保留任务上平均提升3.1%，在未见任务上提升4.4%。
原文摘要 · Abstract (English)
The advancement of large language models (LLMs) has enabled the construction of multi-agent systems to solve complex tasks by dividing responsibilities among specialized agents, such as a planning agent for subgoal generation and a grounding agent for executing tool-use actions. Most existing methods typically fine-tune these agents independently, leading to capability gaps among them with poor coordination. To address this, we propose MOAT, a Multi-Agent Joint Alignment Tuning framework that improves agents collaboration through iterative alignment. MOAT alternates between two key stages: (1) Planning Agent Alignment, which optimizes the planning agent to generate subgoal sequences that better guide the grounding agent; and (2) Grounding Agent Improving, which fine-tunes the grounding agent using diverse subgoal-action pairs generated by the agent itself to enhance its generalization capablity. Theoretical analysis proves that MOAT ensures a non-decreasing and progressively convergent training process. Experiments across six benchmarks demonstrate that MOAT outperforms state-of-the-art baselines, achieving average improvements of 3.1% on held-in tasks and 4.4% on held-out tasks.
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