Researchers at Nvidia and the University of Hong Kong have released Orchestrator, an 8-billion-parameter model that coordinates different tools and large language models (LLMs) to solve complex problems. In their experiments, Orchestrator achieved higher accuracy at a lower cost than much larger models in tool-use benchmarks, while also aligning with user preferences on which tools to use for a given query.The model was trained through ToolOrchestra, a new reinforcement learning (RL) framework for training small models to act as intelligent coordinators. The approach is based on the idea that a small "orchestrator" managing a diverse team of specialized models and tools can be more effective and efficient than a single, monolithic AI system. The findings suggest that this composite approach could pave the way for more practical and scalable AI reasoning systems in the enterprise.The limits of current LLM tool useGiving LLMs access to external tools is a promising way to exten