SKT：通过验证的合成数据生成实现大规模技能使用训练

摘要

技能已成为为语言模型智能体配备可复用程序性知识的重要机制。然而，仅提供技能并不能保证现有模型能够有效地识别、应用和协调技能。为了提升技能使用能力，我们引入了SKT，一种经过验证的数据合成流程，它利用大量智能体技能构建基于技能的任务和可执行轨迹。SKT选择合适的单技能和多技能配置，通过基于规则和基于智能体的验证并辅以反馈引导的修复来合成任务，且仅保留充分使用了全部所需技能的成功轨迹。SKT利用2000个公开技能，生成了4000个任务包和27164条经过验证的轨迹。基于相同的流程和独立的测试池，我们进一步构建了SkillEval，这是一个用于评估技能使用的留出式可执行基准。跨多种模型、基准和智能体框架的实验表明，在SKT生成的轨迹上进行监督微调能够持续提升技能使用性能。验证消融实验、跨框架评估和扩展实验进一步证明，这些收益依赖于高质量监督，不仅限于单一智能体接口，并随着技能覆盖范围的扩大而增加。综合来看，这些结果确立了经过验证的数据合成作为技能使用训练的一种有效且可扩展的方法。

English

Agent skills have become an important mechanism for equipping language-model agents with reusable procedural knowledge. However, providing skills alone does not guarantee that current models can effectively identify, apply, and coordinate them. To improve skill-use capabilities, we introduce SKT, a verified data synthesis pipeline that constructs skill-grounded tasks and executable trajectories from large collections of agent skills. SKT selects suitable single-skill and multi-skill configurations, synthesizes tasks through rule-based and agent-based verification with feedback-guided repair, and retains only successful trajectories that substantially use every required skill. Using 2,000 public skills, SKT produces 4,000 task packages and 27,164 verified trajectories. Based on the same pipeline and a disjoint test pool, we further construct SkillEval, a held-out executable benchmark for evaluating skill use. Experiments across diverse models, benchmarks, and agent harnesses show that supervised fine-tuning on SKT-generated trajectories consistently improves skill-use performance. Verification ablations, cross-harness evaluation, and scaling experiments further demonstrate that these gains depend on high-quality supervision, extend beyond a single agent interface, and increase with broader skill coverage. Together, these results establish verified data synthesis as an effective and scalable approach for skill-use training.