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English(EN) When does RL actually work for training an LLM coordinator? The Conductor (ICLR 2026) trains a 7B model to write the communication topology and per-agent instru

The Conductor LLM 为最佳通信拓扑训练代理

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了“The Conductor”，一个拥有70亿参数的模型，旨在优化多代理LLM系统的通信拓扑和指令。该模型将在即将发布的ICLR 2026论文中详细介绍，它利用递归自应用来处理复杂查询。The Conductor在与现有多个代理的基线模型相比时，表现出了卓越的性能，在模型调用次数更少的情况下取得了可比的结果，并且现已集成到Sakana的Fugu-Ultra产品中。 AI

影响 该模型可以通过优化通信和任务委派来提高多代理LLM系统的效率和有效性。

排序理由 该集群描述了一个新模型及其在研究论文中展示的能力。[lever_c_demoted from research: ic=1 ai=1.0]

在 Mastodon — sigmoid.social 阅读 →

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The Conductor LLM 为最佳通信拓扑训练代理

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该集群描述了一个新模型及其在研究论文中展示的能力。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. Mastodon — sigmoid.social TIER_1 English(EN) · BenjaminHan ·

    强化学习何时能真正用于训练 LLM 协调器？The Conductor (ICLR 2026) 训练了一个 7B 模型来编写通信拓扑和每个代理的指令

    When does RL actually work for training an LLM coordinator? The Conductor (ICLR 2026) trains a 7B model to write the communication topology and per-agent instructions for a pool of LLMs, calling itself recursively on hard queries. It beats multi-agent baselines at roughly three m…

    链接 benjaminhan.net/…/20260629-conductor-llm-…

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