SkillGrad:像梯度下降一样优化智能体技能
阅读原文· arxiv.org针对智能体技能不可靠的问题,SkillGrad 提出了一种受梯度下降启发的优化框架。该框架将技能包视为可优化的结构化参数,利用任务执行产生的轨迹级损失证据生成基于文本的梯度,并通过动量智能体积累诊断模式以稳定优化。最终由基于大语言模型的修补器执行参数更新。在 SpreadsheetBench Verified 和 WikiTableQuestions 上的评估显示,SkillGrad 在两个骨干大语言模型上均优于基于训练的技能进化基线,平均性能提升6.7个百分点。消融实验验证了动量机制与对比诊断方法的有效性。
Agent skills provide a lightweight way to adapt LLM agents to specialized domains by storing reusable procedural knowledge in structured files. However, whether downloaded from third parties or self-generated, these skills are often unreliable, incomplete, or outdated. Existing skill-evolution methods often address these deficiencies through heuristic reflections without an explicit optimization formulation. In this paper, we propose SkillGrad, a gradient-descent-inspired framework for optimizing agent skills. SkillGrad treats the skill package as a structured parameter to optimize in a gradient descent fashion: task executions provide trajectory-level loss evidence, automatic diagnoses then provide text-based gradients that indicate the correction directions. To stabilize optimization across iterations, a momentum agent accumulates recurring diagnostic patterns into a persistent memory overlay. Finally, an LLM-based patcher executes the parameter update by applying layer-aware edits to the skill package. Evaluated on SpreadsheetBench Verified and WikiTableQuestions, SkillGrad consistently outperforms training-based skill evolution baselines across two backbone LLMs, improving over the strongest training-based baseline by 6.7 percentage points on average. Ablations further show that momentum and contrastive diagnosis both contribute to the final skill quality.