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 extend their capabilities beyond their training data and into agentic tasks. By calling on resources like search engines and code interpreters, AI agents can improve their accuracy and perform in-app tasks.However, in the accompanying paper, the researchers argue that the current approach to building tool-using agents doesn’t harness the full potential of this paradigm. Most systems equip a single, powerful model with a set of basic tools like a web search or a calculator. They argue that humans, when reasoning, “routinely extend themselves by calling upon resources of greater-than-human intelligence, from domain experts to sophisticated processes and software systems.” Accordingly, LLMs should be able to interact with a wide range of tools in different capacities.The tool orchestration paradigmThe paper proposes a shift from a single-model system to a composite one, managed by a lightweight “orchestrator” model. The orchestrator’s job is to analyze a complex task and b …
