Skill Self-Play uses co-evolving skills to push LLM capability
QwenBusinessUnit-Edu · hf · 2026-07-27
- The paper argues that current self-evolution methods for LLMs face a trade-off: environment-bound methods verify well but stay narrow, while open-ended self-generation is diverse but weakly verifiable.
- It proposes Skill Self-Play (Skill-SP), which uses agent skills as a middle ground between diversity and reliable verification.
- Skill-SP has three components: a proposer, a solver, and a dynamic skill controller.
- In a reinforcement-learning loop, the proposer creates tasks conditioned on sampled skills, the solver searches for solutions, and the controller updates and expands the skill library from execution feedback.
- The authors report consistent gains on tool-use and reasoning benchmarks, including notable recoveries for initially misaligned models.
- Code is available on GitHub.
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