Co-Skill: A Collaborative Communication Framework for Skill Evolution

RESUMO

Agent evolution through skills becomes critical for LLM-based agents to iteratively improve task success rate. Hybrid evolution is a cost-efficient paradigm where a cloud LLM analyzes and generates skills while an edge SLM executes and internalizes them. However, existing hybrid methods, such as SkillRL, still suffer from low success rate and high token usage. We find this stems from blind communication: the cloud cannot perceive the edge's execution capability, while the edge does not understand the cloud's analysis needs. We thus propose the Collaborative Communication Framework (CCF) to achieve effective edge-cloud evolution. CCF is realized via three techniques: (1) a cloud-aware prefix-merged trajectory trie where the edge compresses trajectories by merging shared prefixes and pinpointing divergence points for efficient cloud analysis, (2) an edge-aware progressive skill tree where the cloud progressively builds a hierarchical skill tree to match edge SLM execution capability, and (3) a collaborative skill evolution scheme upon these two trees that evolves cloud LLM and edge SLM in a separated way to jointly improve task success rate. Experiments across ALFWorld and WebShop show that CCF reduces LLM+SLM tokens by 15.6%--41.9% over state-of-the-art hybrid methods while consistently improving 25.8%--76.4% task success rate.