Bottom line: Skill Self-Play combines task generation, solution search, and dynamic skill control in a reinforcement learning loop to achieve both task diversity and training reliability.
Researchers present a framework called Skill Self-Play that trains LLMs through self-play with dynamic skills—without manual annotations and with reliable feedback.
The prior dilemma in interactive LLM training presents itself as follows: environment-grounded methods deliver reliable feedback but restrict learning to narrow domains. Open-ended task generation enables more broadly distributed learning goals but is difficult to verify and allows faulty signals to enter training. Skill Self-Play resolves this conflict by introducing skills as a central intermediate layer: each skill guarantees deep and verifiable behavior in a specific scenario, while dynamic routing across multiple skills preserves open task diversity.
The framework orchestrates three co-evolution components in a reinforcement learning loop: a Proposer generates challenging tasks based on dynamically selected skills. A Solver explores potential solutions and extends its capabilities. A Skill Controller collects execution feedback and continuously updates the skill library. This interaction closes the gap between structured verification and open exploration.
Empirical tests on tool-use and reasoning benchmarks show that Skill Self-Play as an evolution engine consistently improves the performance of existing models. Even initially weakly trained models experience significant performance jumps. The code is available at https://github.com/Qwen-Applications/skill-self-play.
Source: arxiv.org · Published 23 July 2026
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