ExpVoyager: Direct Experience Navigation for Dynamic Agent Skill Synthesis
ExpVoyager lets LLM agents synthesize reusable skills by navigating past experience on demand.
ExpVoyager treats agent skill synthesis as dynamic navigation over accumulated experience rather than freezing procedural knowledge before future tasks are known. A skill curator explores raw trajectories at different views and resolutions, extracting reusable procedures while tracking unmet knowledge needs. Experiments show better downstream task performance, continued gains as the experience space grows, and compatibility with existing skills under efficient access.
- Skill synthesis is reframed as on-demand navigation of past trajectories.
- A skill curator searches experience at multiple views and resolutions.
- It tracks remaining knowledge needs to decide where to look next.
- Gains grow as the experience space scales and existing skills remain compatible.
From the original article
1 min read at huggingface.co
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.
By Kwangwook Seo, Dongha Lee · huggingface.co
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