Meta 与 Carnegie Mellon 研究：训练小模型从大量工具中选择所需工具

New Meta and Carnegie Mellon paper finds that small models trained with RL pick the right tools more often than a prompted 402B Llama-4-Maverick. Agents can face more than 10,000 tools, and their descriptions cannot all fit in context. Retrievers only rank by relevance, so recent setups have Claude or ChatGPT pick the tools. They trained Qwen3 models to split a request into steps, search each step with plain keyword search, and pick 1 tool per step. Training used teacher-written examples, then RL that rewards useful search steps and exact picks. On ToolBench, the trained 8B model picked exactly the right tools 41.5% of the time, versus 30.6% for Maverick's best prompted setup. With no retraining, it beat Maverick on 3 of 4 new tool benchmarks and improved when given a stronger retriever. If your agent chooses from a large tool catalog, an RL-trained small model can handle selection instead of a prompted large one.