Abstract: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.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2506.23149 [cs.CL] |
| (or arXiv:2506.23149v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2506.23149 arXiv-issued DOI via DataCite |
From: Dingzirui Wang [view email]
[v1]
Sun, 29 Jun 2025 08:57:09 UTC (295 KB)
[v2]
Tue, 26 May 2026 02:07:39 UTC (308 KB)
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