Skill Self-Play: Pushing the Frontier of LLM Capability with Co-Evolving Skills

LLM training is shifting from manual design and annotation to interaction-driven self-evolution. However, existing self-evolutionary methods face a fundamental dilemma between task diversity and verification reliability: environment-bound methods obtain precise feedback but confine learning to narrow domains, while open-ended self-generation broadens the task space but lacks reliable verification, allowing misleading rewards to pollute the training loop. We identify agent skills as a powerful mi

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ethics.ai (23 July 2026), “Skill Self-Play: Pushing the Frontier of LLM Capability with Co-Evolving Skills,” evidence record 13600, https://ethics.ai/record/13600 (originally published by HuggingFace Daily Papers).

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