SkillGrad: Optimizing Agent Skills Like Gradient Descent
Quick Take
SkillGrad introduces a gradient-descent-inspired framework for optimizing agent skills, outperforming training-based methods by an average of 6.7 percentage points on benchmarks like SpreadsheetBench Verified and WikiTableQuestions. By utilizing trajectory-level loss evidence and a momentum agent for stabilization, SkillGrad enhances the reliability of agent skills in specialized domains.
Key Points
- SkillGrad optimizes agent skills using a gradient descent approach.
- It improves skill quality by leveraging trajectory-level loss evidence.
- A momentum agent stabilizes optimization by accumulating diagnostic patterns.
- SkillGrad outperforms training-based baselines across two backbone LLMs.
- Average improvement over the strongest baseline is 6.7 percentage points.
Article Content
From source RSS / original summaryarXiv:2605. 27760v1 Announce Type: new Abstract: 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 However, whether downloaded from third parties or self-generated, these skills are often unreliable, incomplete, or outdated.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.
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