SkillAdam: Stable and Efficient Skill Evolution for Agents
Agent skills provide a lightweight way to equip frozen language-model agents with domain knowledge and procedural guidance, yet obtaining high-quality skills remains costly and difficult to scale. Expert-written skills require substantial human effort. Recent skill self-evolution methods automate an iterative loop that uses execution feedback to revise skills, but their heuristic update strategies often yield unstable optimization and low iteration efficiency. We identify two challenges in realizing stable and efficient skill self-evolution. Direction Stability requires effective corrections to accumulate rather than be overwritten by iteration-local feedback. Update Adaptivity requires t...
| Authors | Gaoyuan Li, Meihao Fan, Yizhe Liu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 8 September 2026 |
| Last updated at source | 8 September 2026 |
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