When training tool-use agents, prioritize diversity of tools and data sources over raw task quantity—this scales better and produces more capable agents than previous synthetic training approaches.
This paper studies how to train better LLM agents that use tools to complete multi-step tasks. The authors created SynthAPI, a system that generates synthetic training environments with controllable diversity across tools, tasks, and databases.