💡 The paper introduces OpenSkill, a framework that enables self-evolving agents to develop skills and verification signals from scratch using open-world resources without target-task supervision. The problem addressed is that existing approaches to self-evolving agents require a usable learning loop, such as curated skills or successful trajectories, which may not be available in real-world deployment scenarios. OpenSkill solves this problem by bootstrapping the learning loop, acquiring grounded knowledge and verification anchors from documentation, repositories, and the web, synthesizing them into transferable skills, and refining those skills against self-built virtual tasks.

The method used in OpenSkill involves three main steps. First, it acquires knowledge and verification anchors from open-world resources. Second, it synthesizes this knowledge into transferable skills. Third, it refines these skills against self-built virtual tasks grounded in the anchors, rather than in target answers. This approach allows the agent to develop skills and verification signals without requiring target-task supervision.

The results of OpenSkill are impressive, with the framework attaining the best automated pass rate across three benchmarks and two target agents, while satisfying the no-supervision constraint. Analysis of the results shows that the skills learned by OpenSkill transfer across models without requiring model-specific adaptation, and the self-built verifier aligns with ground-truth outcomes despite never accessing them. Overall, OpenSkill provides a novel approach to open-world self-evolution, enabling self-evolving agents to develop skills and verification signals from scratch using open-world resources, and achieving high automated performance across benchmarks.