让大模型技能进化更贴合任务需求，避免无效或遗漏。
AlignEvoSkill: Towards Knowledge-Aware and Task-Aligned Agent Skill Evolution
- 通过知识标签与任务对齐双目标优化技能演化
- 在3个基准上相对基线提升34.7%，达到新SOTA
- 适合需要高效、精准技能迭代的智能体研究者
可复用的技能在提升基于大语言模型的智能体性能中起关键作用，但现有技能演化方法常无法保证所生成技能既覆盖任务所需知识，又与目标任务保持一致。这导致演化出的技能可能不完整或无关。为此，我们提出AlignEvoSkill，一种联合建模知识覆盖度与任务对齐性的技能演化框架。给定失败的任务轨迹，该框架首先识别任务相关的知识标签，检索互补的已有技能，并将其适配为填补知识缺口的候选技能；随后基于知识覆盖度与任务对齐度得分的联合筛选准则，选择高质量候选技能。在3个基准和4种LLM主干模型上的实验表明，AlignEvoSkill相比非演化基线取得34.7%的相对提升，并在更低成本下实现技能演化的新最佳性能。
原文摘要 · Abstract (English)
Reusable skills play a key role in improving LLM-based agents, but existing skill-evolution methods often fail to ensure that evolved skills both cover the knowledge required by the task and remain aligned with the target task. As a result, evolved skills could be incomplete or irrelevant. To address this limitation, we propose AlignEvoSkill, a skill-evolution framework that jointly models knowledge coverage and task alignment. Given failed task trajectories, AlignEvoSkill first identifies task-relevant knowledge tags, retrieves complementary prior skills, and adapts them into candidate skills that address missing knowledge. It then selects high-quality candidates using a joint filtering criterion based on knowledge-coverage and task-alignment scores. Experiments on 3 benchmarks with4 LLM backbones show a 34.7% relative gain of AlignEvoSkill over the non-evolution baseline and achieves a new SOTA in skill evolution with lower cost.
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