Meta and Carnegie Mellon University paper: RL-trained small model surpasses 402B Llama-4-Maverick in tool selection accuracy
A new paper from Meta and Carnegie Mellon University finds that a small model trained with reinforcement learning selects the correct tool more often than the prompt-driven 402B Llama-4-Maverick. The researchers trained a Qwen3 model to break requests into steps, search step by step with keywords, and select 1 tool per step; training first used teacher-written examples, then reinforcement learning rewarded effective search steps and precise selection. On ToolBench, the trained 8B model selected the correct tool precisely 41.5% of the time, versus 30.6% for Maverick's best prompt setting; without retraining, it surpassed Maverick on 3 of 4 new tool benchmarks, and further improved after switching to a stronger retriever.
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The original text provides specific comparison numbers between a small model and a 402B large model on tool selection, which can serve as a reference when evaluating agent tool retrieval approaches.
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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.