Write, Execute, Refine: From Skill Follower to Skill Optimizer via Reinforcement Learning from Execution Feedback
Expert-written natural language skills can improve tool-using agents, yet agent-authored skills perform 8--11 points worse than using no skill. While inference-time loops can repair individual skills per task, they leave the underlying generator's refinement capability unimproved. We study how to organize execution experience from intermediate skills into training states for an optimizer. We introduce WER(Write, Execute, and Refine), a multi-phase framework that trains a Skill Optimizer outside a frozen executor. The optimizer proposes skills, a frozen agent executes each repeatedly, and a programmatic verifier scores the outcomes. These scores serve as group-relative rewards and filter f...
| Authors | Kang Peng, Zhiwei Zhang, Yichen Zhang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 18 August 2026 |
| Last updated at source | 13 October 2026 |
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