New Conductor model learns to orchestrate LLMs for better performance

Researchers have developed a "Conductor" model trained with reinforcement learning to coordinate multiple large language models. This Conductor model learns to establish communication pathways and craft specific instructions for worker LLMs, optimizing their collaboration. A 7-billion parameter Conductor demonstrated superior performance on benchmarks like LiveCodeBench and GPQA, surpassing individual models and achieving state-of-the-art results. The system can adapt to various open and closed-source agents and even uses itself as a worker for recursive improvements. AI

IMPACT Introduces a novel RL-based approach for orchestrating multiple LLMs, potentially improving performance on complex reasoning tasks.

RANK_REASON This is a research paper describing a novel model architecture and training methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• Conductor
• GPQA
• LiveCodeBench
• LLM
• Stefan Nielsen

• paper
• model release

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