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 de 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.
Agents can face more than 10,000 tools, and their descriptions cannot all fit in context. Retrievers only rank by relevance, so de 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.
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Oct 24, 2026 · 12:28 AM UTC
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@rohanpaul_ai Training on selection teaches a model what belongs in the prompt long before it learns what to do with the tool once it's picked.
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