利用版本变更经验提升技能自进化能力，效果更优且迁移更强。
VCE-Skill: Enhancing Skill Self-Evolution with Version-Change Experience
- 从公开技能版本变更中提炼可复用的进化先验知识
- 在任务轨迹基础上融合外部经验，平均得分提升3.20~4.98点
- 适合需要持续优化技能的智能体系统，尤其关注迁移性能
智能体依赖可复用技能来编码任务知识、工具使用流程与验证规则。现有技能自进化方法主要基于当前任务执行轨迹进行修正，忽视了公共技能版本历史中积累的进化知识。初步研究发现二者具有明显互补性：公共技能变更提供可复用的进化先验，而轨迹则提供当前任务的具体证据。受此启发，我们提出VCE-Skill，将噪声大且与实现相关的公共技能变更转化为结构化、可复用的版本变更经验，并自适应地与基础演进器生成的轨迹提案融合，从而利用外部经验的同时保留任务特异性证据。大量实验表明，VCE-Skill显著提升技能自进化性能，平均得分提高3.20–4.98分；迁移实验进一步显示，生成的技能具备更强的跨模型迁移能力。本工作揭示了公共技能版本变更作为未被充分挖掘的有效先验知识来源，推动了以轨迹驱动的技能自进化发展。
原文摘要 · Abstract (English)
Agents increasingly rely on reusable skills to encode task knowledge, tool-use procedures, and validation rules. Existing skill self-evolution methods primarily revise skills using execution trajectories collected from current tasks, leaving the evolution knowledge accumulated in public skill version histories largely untapped. Our pilot study reveals a clear complementarity between the two sources: public skill changes provide reusable evolution priors, whereas trajectories provide evidence grounded in the current task. Motivated by this, we propose VCE-Skill, which distills noisy and implementation-specific public skill changes into reusable, structured version-change experience and adaptively fuses it with trajectory-derived proposals from the base evolver, thereby exploiting external experience while retaining task-specific evidence. Extensive experiments demonstrate that VCE-Skill improves skill self-evolution, increasing mean scores by 3.20--4.98 points; transfer experiments further show that the resulting skills achieve stronger cross-model transfer performance. Our work highlights public skill version changes as a previously underexplored yet effective source of prior knowledge and advances trajectory-driven skill self-evolution.
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