Alibaba Qwen proposes Skill Self-Play to co-evolve verified agent skills

rohanpaul_ai · x · 2026-07-28

Alibaba’s Qwen team proposes Skill Self-Play, a co-evolutionary framework for training LLM agents.

The paper targets a core self-improvement problem: if a model generates its own training tasks, it either stays in narrow, easy-to-verify settings or produces noisy, unreliable tasks.
The solution is a growing skill library that guides what tasks to generate, how to generate them correctly, and how to verify them.
Skills are used primarily to create better training data, not just to answer questions at inference time.
The system combines a proposer, a solver, and a dynamic skill controller in a reinforcement-learning loop.
According to the authors, this lets task generation and verification co-evolve, bridging structured verification and open-ended exploration.
They report gains on tool-use and reasoning benchmarks, and say the method consistently pushes performance ceilings on competent backbones while reducing catastrophic failure on initially undertrained models.

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