Meta and Carnegie Mellon study finds an RL-trained 8B model beats Llama-4-Maverick at tool selection
Photo: Crypto Briefing
A Qwen3-8B model post-trained with reinforcement learning hit 41.5% on ToolBench G3, ahead of the roughly 402B-parameter Llama-4-Maverick at 30.6% 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.
Source: Crypto Briefing
