A new method called Grounded Skill Synthesis from Code at Scale aims to give AI agents reusable, transferable procedural knowledge by synthesizing skills directly from code, according to the research. The approach targets two limitations of existing methods: trajectory-based synthesis, which requires interactions with specific environments, and document-derived skills, which may lack grounding. The work addresses scalable skill acquisition for agentic intelligence beyond prior experience.
Reusable skills give agents transferable procedural knowledge, making scalable acquisition essential for extending agents beyond prior experience. Existing methods face two limitations: trajectory-based synthesis requires interactions with specific environments, while document-derived skills may lac
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