OpenSkill: Open-World Self-Evolution for LLM Agents
Summary
OpenSkill is a framework for LLM agents to self-evolve skills and verification signals from open-world resources without target-task supervision, achieving high performance across benchmarks.
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Paper page - OpenSkill: Open-World Self-Evolution for LLM Agents
Source: https://huggingface.co/papers/2606.06741
Abstract
OpenSkill enables self-evolving agents to develop skills and verification signals from scratch using open-world resources without target-task supervision, achieving high automated performance across benchmarks.
Self-evolving agentsrequires adaptation after deployment, but existing approaches assume a usable learning loop, such as curated skills, successful trajectories, or verifier signals. Realopen-world deployments may provide none of these, offering only a task prompt. In this work, we study open-world self-evolution, where an agent must build both its skills and its ownverification signalsfrom scratch, using open-world resources but no target-task supervision. We propose OpenSkill, a framework that bootstraps this loop: it acquiresgrounded knowledgeand verification anchors from documentation, repositories, and the web, synthesizes them intotransferable skills, and refines those skills againstself-built virtual tasksgrounded in the anchors rather than in target answers. The open world thus supplies both the knowledge to be learned and a supervision-independent practice environment, with target-task supervision reserved for final evaluation. Across three benchmarks and two target agents, OpenSkill attains the bestautomated pass ratewhile satisfying the no-supervision constraint. Analysis shows its skills transfer across models without model-specific adaptation, and its self-built verifier aligns with ground-truth outcomes despite never accessing them.
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