![[2512.04388] Learning to Orchestrate Agents in Natural Language with the Conductor](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2512.04388] Learning to Orchestrate Agents in Natural Language with the Conductor
Summary
The paper introduces the Conductor model, which utilizes reinforcement learning to optimize coordination strategies among large language models (LLMs), achieving state-of-the-art performance in reasoning tasks.
Why It Matters
This research highlights the potential of reinforcement learning to enhance collaboration among LLMs, paving the way for more efficient AI systems. As AI becomes increasingly integrated into various applications, understanding how to orchestrate multiple models effectively is crucial for maximizing their capabilities.
Key Takeaways
- The Conductor model learns to optimize communication among LLMs.
- It achieves superior performance by discovering effective coordination strategies.
- The model adapts to various agent pools, enhancing flexibility.
- Recursive topologies can be formed, allowing for dynamic scaling.
- This work demonstrates the potential of RL in unlocking model coordination.
Computer Science > Machine Learning arXiv:2512.04388 (cs) [Submitted on 4 Dec 2025 (v1), last revised 20 Feb 2026 (this version, v3)] Title:Learning to Orchestrate Agents in Natural Language with the Conductor Authors:Stefan Nielsen, Edoardo Cetin, Peter Schwendeman, Qi Sun, Jinglue Xu, Yujin Tang View a PDF of the paper titled Learning to Orchestrate Agents in Natural Language with the Conductor, by Stefan Nielsen and 5 other authors View PDF HTML (experimental) Abstract:Powerful large language models (LLMs) from different providers have been expensively trained and finetuned to specialize across varying domains. In this work, we introduce a new kind of Conductor model trained with reinforcement learning to automatically discover powerful coordination strategies among LLMs. Our Conductor learns not only to design targeted communication topologies for effective agent-to-agent collaboration, but also to prompt engineer focused instructions to the LLMs to maximally leverage their individual capabilities. We show that, by learning optimal coordination strategies over pools of powerful worker LLMs, a 7B Conductor achieves significant performance gains beyond any individual worker, attaining state-of-the-art results in challenging reasoning benchmarks, such as LiveCodeBench and GPQA. By training with randomized agent pools, our conductor effectively adapts to arbitrary sets of open- and closed-source agents, meeting any user requirements. Furthermore, allowing the Conductor to ...
