Skill-α uses reinforcement learning to generate better agent skills
CUHKDBGROUP · hf · 2026-08-04
Skill-α uses reinforcement learning to generate better agent skills
This paper studies how to automatically generate reusable skills for LLM agents from documents or past experience.
- Existing methods rely heavily on heuristics or pipeline-specific consolidation steps.
- The proposed Skill-α treats skill creation as a sequential editing process, so each edit can be evaluated individually.
- It introduces a rollback reward that compares downstream execution using the original versus edited skill on anchored queries.
- On the main GPT-4o worker, the method improves average downstream success rates by 3.3 points on CL-Bench and 6.7 points on tau2-bench over the strongest baseline.
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