Learning Reusable Agent Subroutines from Experience
The method identifies recurring reasoning procedures within successful agent traces and synthesizes them into a reusable library of agent subroutines called reasoning primitives. This induced library, which is a typed inventory of recurring moves, outperforms the original agent's performance across various tasks by significant margins. It proves that recovered reasoning structure can be more reliable than the source policy itself.