ExpVoyager Reframes Agent Skill Synthesis as On-Demand Navigation Over Raw Experience
yonsei-dli · hf · 2026-09-30
Researchers at Yonsei DLI propose ExpVoyager, addressing a key weakness in self-evolving LLM agents: existing skill-synthesis approaches compress past experience into fixed procedural knowledge before downstream needs are known, discarding critical knowledge and keeping irrelevant details.
ExpVoyager recasts skill synthesis as dynamic navigation over accumulated trajectories: a skill curator browses raw experience at multiple views and resolutions, identifying reusable procedural knowledge on demand while tracking unmet knowledge needs to guide further exploration.
Experiments show consistent downstream gains, continual improvements as the experience space scales, and compatibility with existing skill libraries under efficient experience access.
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