SkillGrad: Gradient-Descent-Inspired Framework for Optimizing LLM Agent Skills
Researchers propose SkillGrad, a framework that optimizes agent skills for LLMs using a gradient-descent-inspired approach. Skills, stored as structured files, adapt agents to specialized domains but often suffer from unreliability or incompleteness. SkillGrad treats skill packages as parameters, using task execution losses as trajectory-level evidence and automatic diagnoses to generate text-based gradients for correction. A momentum agent accumulates recurring diagnostic patterns into persistent memory, and an LLM-based patcher executes updates. The paper is available on arXiv under identifier 2605.27760.
Key facts
- SkillGrad is a gradient-descent-inspired framework for optimizing agent skills.
- Agent skills are lightweight, structured files for adapting LLM agents to specialized domains.
- Existing skill-evolution methods rely on heuristic reflections without explicit optimization.
- SkillGrad treats skill packages as parameters to optimize in a gradient descent fashion.
- Task executions provide trajectory-level loss evidence for optimization.
- Automatic diagnoses generate text-based gradients indicating correction directions.
- A momentum agent accumulates recurring diagnostic patterns into persistent memory overlay.
- An LLM-based patcher executes the updates based on the accumulated memory.
Entities
Institutions
- arXiv