ToolOrchestra

We introduce ToolOrchestra, a method for training small orchestrators that coordinate the use of intelligent tools. By using both tools and specialized models, ToolOrchestra surpasses GPT-5 while being much more efficient. Given a task, the Orchestrator alternates between reasoning and tool calling in multiple turns to solve it. The Orchestrator interacts with a diverse tool set, including basic tools (e.g., web search, code interpreter), specialized LLMs (e.g., coding models, math models), and generalist LLMs (e.g., GPT-5, Llama-Nemotron-Ultra-253B, Claude Opus 4.1). During training, Orchestrator is jointly optimized by outcome, efficiency, and preference rewards via end-to-end reinforcement learning. To aid RL training, we develop an automatic pipeline to synthesize both environment and tool-call tasks at scale.