Skill Self-Play: Pushing the Frontier of LLM Capability with Co-Evolving Skills
This paper presents a new method for training large language models (LLMs) that allows them to learn and improve by themselves, without needing constant human input.
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- 1 Skill Self-Play allows LLMs to evolve their skills through self-generated tasks.
- 2 It balances the need for diverse tasks with the requirement for reliable feedback.
- 3 The framework consists of three main components: a proposer, a solver, and a skill controller.
- 4 Empirical tests show that this method significantly enhances the capabilities of LLMs.
Introduction
The introduction discusses the shift in LLM training towards self-evolution and the challenges faced by existing methods in balancing task diversity and verification reliability. It highlights the importance of self-play in enhancing LLM capabilities across various domains.
Methodology
The methodology outlines the Skill Self-Play framework, detailing how it orchestrates the joint optimization of proposer and solver policies alongside a skill controller to create a continuous co-evolutionary loop for task generation.
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Skill Self-Play Objective
This section formalizes the objectives of Skill-SP, describing how tasks are generated and verified through a structured reward system that prevents reward hacking and promotes valid task synthesis.
Figures Explained
The paper’s visual material highlights the workflow and the main system components.
- Figure 2 |: Figure2| Performance footprint. Skill-SP broadly expands Qwen3-4B-Ins capabilities across tool-calling and logical reasoning. Notably, Unguided SP is absent from the latter as it fails to synthesize valid puzzles.
- Figure 3 |: Figure 3 | Overview of Skill Self-Play. An evolving skill library routes task-generation guidance to the proposer, which generates candidate tasks that undergo validity verification. Valid candidates are ranked by frontier reward to construct the solver curriculum. The resulting signals drive co-evolutionary proposer and solver updates, while validation failures, novel samples, and task-level statistics trigger skill refinement, pruning, and induction, distilling training feedback into reusable skills for subsequent task generation.
- 34: Verify candidates and compute R propose ; retain valid candidates for the solver curriculum; Rank valid candidates by R propose to build the mixed frontier curriculum D(\ud835\udc61) ; 5.
- Figure 4: Skill-only ablation. Figure4| Tool-call ablations. Skill-SP improves more steadily than Unguided SP, while skill-only data underperforms the mixed pool.
- Figure 5 |Figure 6 |: Figure5| Data-loop diagnostics for Qwen3-4B-Instruct tool-call self-play. Skill-stream records stay closer to the empirical frontier than both the exploration stream and Unguided SP, while the overall Skill-SP pool yields broader task coverage in the question-embedding space, and the skill library is continually induced, updated, and retired across self-play iterations.
Frequently Asked Questions
This paper presents a new method for training large language models (LLMs) that allows them to learn and improve by themselves, without needing constant human input.
The introduction discusses the shift in LLM training towards self-evolution and the challenges faced by existing methods in balancing task diversity and verification reliability. It highlights the importance of self-play in enhancing.
The methodology outlines the Skill Self-Play framework, detailing how it orchestrates the joint optimization of proposer and solver policies alongside a skill controller to create a continuous co-evolutionary loop for task generation.
Yes. PDFDigest can turn this paper into a structured explanation, key takeaways, visual summaries, and a narrated video when available.