English(EN) Learning to Orchestrate Agents in Natural Language with the Conductor

新的 Conductor 模型学会编排大型语言模型以获得更好性能

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-07 04:00

研究人员开发了一个名为“Conductor”的模型，该模型通过强化学习进行训练，以协调多个大型语言模型。该 Conductor 模型学会建立通信路径并为工作模型制定具体指令，从而优化它们的协作。一个拥有 70 亿参数的 Conductor 在 LiveCodeBench 和 GPQA 等基准测试中表现出色，超越了单个模型，并取得了最先进的成果。该系统可以适应各种开源和闭源代理，甚至使用自身作为工作代理以实现递归改进。 AI

影响引入了一种新颖的基于强化学习的方法来编排多个大型语言模型，有可能提高复杂推理任务的性能。

排序理由这是一篇描述新颖模型架构和训练方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Stefan Nielsen, Edoardo Cetin, Peter Schwendeman, Qi Sun, Jinglue Xu, Yujin Tang · 2026-05-07 04:00

用自然语言学习协调智能体与 Conductor

arXiv:2512.04388v5 Announce Type: replace 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…

本文如何被排名

报道来源 [1]

用自然语言学习协调智能体与 Conductor

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