English(EN) NoLoCo: No-all-reduce Low Communication Training Method for Large Models

新的 NoLoCo 方法大幅削减大语言模型训练通信成本

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了一种名为 NoLoCo 的新颖优化方法，旨在显著降低大语言模型训练期间的通信开销。与仍需要全局同步的现有低通信方法不同，NoLoCo 通过在随机选择的子组内部分平均模型权重来避免这种情况。与完全分片数据并行训练和 DiLoCo 等方法相比，这种方法导致通信强度较低。实证结果表明，NoLoCo 可以将最终困惑度提高高达 4%，并将实际运行时间收敛速度提高高达四倍。 AI

影响降低了大语言模型训练中的通信瓶颈，可能支持更大模型的训练和更快的迭代周期。

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在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Jari Kolehmainen, Nikolay Blagoev, Semih Kara, John Donaghy, Christopher Nies, O\u{g}uzhan Ersoy · 2026-09-10 04:00

NoLoCo：大型模型的无 all-reduce 低通信训练方法

arXiv:2506.10911v2 Announce Type: replace Abstract: Training large language models is generally done on clusters containing thousands of accelerators, communicating over a high-bandwidth interconnect. Scaling up these clusters is expensive and can become impractical, imposing lim…

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NoLoCo：大型模型的无 all-reduce 低通信训练方法

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