SearchSkill: Teaching LLMs to Use Search Tools with Evolving Skill Banks

• URL: http://arxiv.org/abs/2605.09038v2
• Date: Thu, 14 May 2026 10:12:06 GMT
• Title: SearchSkill: Teaching LLMs to Use Search Tools with Evolving Skill Banks
• Abstract summary: We propose a framework that makes query planning explicit through reusable search skills.<n>Across open-source and closed-source models, SearchSkill improves exact match on knowledge-intensive QA benchmarks.<n>These results suggest that explicit skill-conditioned query planning is a lightweight alternative to treating search as an undifferentiated action.
• Score: 32.240602087886494
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Teaching language models to use search tools is not only a question of whether they search, but also of whether they issue good queries. This is especially important in open-domain question answering, where broad or copied queries often waste retrieval budget and derail later reasoning. We propose \Ours, a framework that makes query planning explicit through reusable search skills. At each step, the model first selects a skill, then generates a search or answer action conditioned on the selected skill card. The skill inventory itself is not fixed: SearchSkill maintains an evolving SkillBank, expands or refines it from recurrent failure patterns, and reconstructs affected trajectories before supervised training. The resulting two-stage SFT recipe aligns training with the inference-time protocol of skill selection followed by skill-grounded execution. Across open-source and closed-source models, SearchSkill improves exact match on knowledge-intensive QA benchmarks and yields better retrieval behavior, including fewer copied first queries, more atomic hop-focused queries, and more correct answers within a small search budget. These results suggest that explicit skill-conditioned query planning is a lightweight alternative to treating search as an undifferentiated action.

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