Meta and Carnegie Mellon study finds an RL-trained 8B model beats Llama-4-Maverick at tool selection

A language model with 8 billion parameters just outscored one roughly fifty times its size. The task was picking the right software tool for a job. Researchers from Meta and Carnegie Mellon University report that a small Qwen3-8B model, post-trained with reinforcement learning, selected the correct tools 41.5% of the time on the ToolBench G3 benchmark. Meta’s own Llama-4-Maverick, a model of approximately 402 billion parameters, scored 30.6% under similar conditions. What the researchers actually built The work appears in an arXiv paper titled “Training Small Language Models for Transferable Tool Selection,” listed as arXiv:2610.31483. It was submitted on October 22, 2026. The problem it tackles is a practical one for AI agents. Agents are models that take actions, such as calling an API or querying a database, rather than just producing text. To do that, they have to choose the right tool from whatever is available. The study focuses on settings where the tool corpus exceeds 10,000 tools. Instead of using proprietary systems like Claude or ChatGPT, the team worked with compact Qwen3 models ranging from 1.7 billion to 8 billion parameters. They paired those models with a three-step...