Skill Self-Play: Pushing the Frontier of LLM Capability with Co-Evolving Skills
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TL;DR - Skill Self-Play is a reinforcement-learning framework in which task generation, problem solving, and an agent skill library co-evolve. It aims to combine open-ended task diversity with reliable, skill-based verification.
- A proposer generates challenging tasks conditioned on dynamically sampled skills.
- A solver explores candidate solutions, while a controller uses execution feedback to update and expand the skill library.
- Dynamic skill routing supports broad exploration while preserving scenario-specific, verifiable execution.
- Evaluations on tool-use and reasoning benchmarks report improvements for capable backbones and initially misaligned models.
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Skill Self-Play: Pushing the Frontier of LLM Capability with Co-Evolving Skills
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Hugging Face upvotes 47
TL;DR - Skill Self-Play is a reinforcement-learning framework in which task generation, problem solving, and an agent skill library co-evolve. It aims to combine open-ended task diversity with reliable, skill-based verification.
- A proposer generates challenging tasks conditioned on dynamically sampled skills.
- A solver explores candidate solutions, while a controller uses execution feedback to update and expand the skill library.
- Dynamic skill routing supports broad exploration while preserving scenario-specific, verifiable execution.
- Evaluations on tool-use and reasoning benchmarks report improvements for capable backbones and initially misaligned models.