ExpVoyager：面向动态智能体技能合成的直接经验导航

摘要

在 LLM 智能体中从经验学习已成为开发能够持续学习并扩展能力的自演化智能体的关键范式。在这一范式中，智能体技能合成已成为一种有前景的解决方案，用于将积累的经验转化为可复用的程序性知识，并作为运行时为智能体提供支持的 harness 系统的重要一层。尽管具有潜力，现有方法大多在下游需求已知之前，就将过去经验抽象为固定的程序性知识，这可能丢弃后来变得关键的知识，同时保留与未来任务无关的实例特定细节。在本文中，我们将智能体技能合成重新表述为在过去经验上的动态导航问题，其中智能体按当前任务需求主动探索累积轨迹，并对经验知识进行有针对性、细粒度的访问。为此，我们提出 ExpVoyager，一种新颖框架，其中技能策展器在不同视图和分辨率下导航原始经验，持续从所观察内容中识别可复用的程序性知识，同时跟踪剩余知识需求，以指导下一步导航位置。大量实验证明了 ExpVoyager 的有效性和通用性，显示其在下游任务性能上持续提升、随经验空间扩展而不断获得收益，并在高效经验访问下与现有技能具有实际兼容性。

English

Learning from experience in LLM agents has become a key paradigm for developing self-evolving agents that continuously learn and expand their capabilities. Within this paradigm, synthesizing the agent skill has emerged as a promising solution for transforming accumulated experience into reusable procedural knowledge, serving as an important layer for the harness system that supplies agents at runtime. Despite its potential, existing approaches largely abstract past experience into fixed procedural knowledge before downstream demands are known, which risks discarding knowledge that later becomes critical while retaining instance-specific details irrelevant to future tasks. In this paper, we reframe agent skill synthesis as a dynamic navigation problem over past experience, where agents actively explore accumulated trajectories on demand for the current task with targeted and fine-grained access to experience knowledge. To this end, we propose ExpVoyager, a novel framework in which a skill curator navigates raw experience across different views and resolutions, continually identifying reusable procedural knowledge from what it observes while tracking remaining knowledge needs that guide where to navigate next. Extensive experiments demonstrate both the effectiveness and versatility of ExpVoyager, showing consistent improvements in downstream task performance, continual gains as the experience space scales, and practical compatibility with existing skills under efficient experience access.