aiqbench analysis
SkillVine addresses the limitation of linear skill evolution in LLM agents by framing the process as a graph search problem to avoid local optima. This framework utilizes a branching exploration strategy that collaboratively searches trunk and branch nodes, significantly outperforming sequential update methods across multiple benchmarks. The approach represents a meaningful advancement in automatic procedural knowledge refinement for autonomous agents.
SkillVine: Agent Skill Evolution via Branching Exploration
arXiv:2609.32731v1 Announce Type: new Abstract: Agent skills encapsulate reusable procedural knowledge that enables LLM agents to perform tasks, and they can be improved automatically using trajectories from interactions with the environment. This is the classic problem of skill evolution. Existing approaches predominately follow a linear evolution paradigm, in which updates are sequentially applied to the latest skill-library version. As a result, they inevitably fall into local optima, leaving many promising evolution paths unexplored. We propose SkillVine, an automatic skill-evolution framework that formulates skill evolution as a graph search problem and employs a branching exploration strategy. Equipped with a trunk-branch collaborative searching mechanism, an intelligent parent-node selector, and an adaptive-granularity update rule, SkillVine achieves a balance between exploration and exploitation. We evaluate SkillVine on 5 benchmarks with two LLMs. Results show that SkillVine discovers better skill-library versions along branches than along the linear trunk and achieves the best test performance in nine of ten benchmark-model combinations.
Abstract:Agent skills encapsulate reusable procedural knowledge that enables LLM agents to perform tasks, and they can be improved automatically using trajectories from interactions with the environment. This is the classic problem of skill evolution. Existing approaches predominately follow a linear evolution paradigm, in which updates are sequentially applied to the latest skill-library version. As a result, they inevitably fall into local optima, leaving many promising evolution paths unexplored. We propose SkillVine, an automatic skill-evolution framework that formulates skill evolution as a graph search problem and employs a branching exploration strategy. Equipped with a trunk-branch collaborative searching mechanism, an intelligent parent-node selector, and an adaptive-granularity update rule, SkillVine achieves a balance between exploration and exploitation. We evaluate SkillVine on 5 benchmarks with two LLMs. Results show that SkillVine discovers better skill-library versions along branches than along the linear trunk and achieves the best test performance in nine of ten benchmark-model combinations.
Artificial Intelligence (cs.AI)
arXiv:2609.32731 [cs.AI]
(or arXiv:2609.32731v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.32731 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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