Learning Globally Reusable Skills for Coding Agents
Automated skill evolution enables Large Language Model (LLM) agents to continuously improve without expensive retraining. However, existing approaches typically treat skill evolution as a sequence of local updates, overlooking relationships among skills and often producing overfitted skill updates that fail to generalize across tasks. We propose GSE, a globalized skill evolution framework that jointly optimizes skill compatibility and skill generalization. To preserve consistency across the skill bank, GSE maintains a Skill Relation Graph (SRG) that explicitly models and co-evolves inter-skill relationships. To improve generalization, GSE performs cluster-based skill consolidation to abst...
| Authors | Chen Yang, Jiashuo Tian, Ziqi Wang et al. |
|---|---|
| Primary category | cs.SE |
| Published | 6 August 2026 |
| Last updated at source | 6 August 2026 |
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