Qwen's Skill Self-Play Framework Boosts LLM Tool Use by 42 Points
May_F1_ · x · 2026-08-03
Alibaba's Qwen team introduced Skill Self-Play (Skill-SP), a co-evolutionary framework to solve the dilemma between task diversity and verification reliability in LLM self-evolution.
The framework features a dynamic skill library and three collaborative roles within a reinforcement learning loop:
- Proposer: Generates challenging tasks conditioned on sampled skills.
- Solver: Explores solutions to push capability boundaries.
- Controller: Updates the skill library based on execution feedback (retiring outdated skills, consolidating new patterns).
Tested on five open models (3B-14B), the framework yielded a maximum increase of 42.9 percentage points in tool-use capabilities and 12 points in logical reasoning. Notably, the skills act only as a training scaffold; the final model does not read the skill library during inference, fully internalizing the learned capabilities.
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