Learning to Orchestrate Agents in Natural Language with the Conductor

Sakana AI

Sakana AI trained a 7-billion-parameter Conductor model using reinforcement learning to manage and coordinate a team of other AI models by writing natural language instructions tailored to each task. The Conductor achieved 83.9% on LiveCodeBench and 87.5% on GPQA-Diamond, surpassing individual models in its pool while dynamically adapting its approach—using single queries for simple questions and constructing multi-step workflows for complex problems. This approach enables AI systems to leverage collective intelligence by learning to delegate tasks across diverse models rather than relying on fixed human-designed workflows.