让语言代理根据任务自动选择最佳工具，提升智能体灵活性。
Towards Adaptive Mechanism Activation in Language Agent
- 构建统一行动框架，用动作统一不同机制
- 通过自探索训练实现任务驱动的机制自适应激活
- 无需专家模型，适合复杂多变的任务场景
语言智能体可被赋予多种机制以实现自主任务完成。当前智能体通常依赖固定机制或预设顺序激活机制，限制了对不同任务结构的适应能力。为此，本文提出自探索驱动的自适应语言智能体机制激活学习方法（ALAMA），旨在不依赖专家模型的情况下优化机制激活的适应性。首先，构建统一框架UniAct，通过动作统一多种机制；随后，采用高效训练的自探索优化方法，使UniAct能根据任务潜在特征自适应地激活合适机制。实验表明，该方法在下游智能体任务中取得显著提升，验证了其在促进动态、上下文敏感机制激活方面的有效性。
原文摘要 · Abstract (English)
Language Agent could be endowed with different mechanisms for autonomous task accomplishment. Current agents typically rely on fixed mechanisms or a set of mechanisms activated in a predefined order, limiting their adaptation to varied potential task solution structures. To this end, this paper proposes \textbf{A}daptive \textbf{L}anguage \textbf{A}gent \textbf{M}echanism \textbf{A}ctivation Learning with Self-Exploration (\textbf{ALAMA}), which focuses on optimizing mechanism activation adaptability without reliance on expert models. Initially, it builds a harmonized agent framework (\textbf{UniAct}) to \textbf{Uni}fy different mechanisms via \textbf{Act}ions. Then it leverages a training-efficient optimization method based on self-exploration to enable the UniAct to adaptively activate the appropriate mechanisms according to the potential characteristics of the task. Experimental results demonstrate significant improvements in downstream agent tasks, affirming the effectiveness of our approach in facilitating more dynamic and context-sensitive mechanism activation.
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