English(EN) DynamiQ: Accelerating Gradient Synchronization using Compressed Multi-hop All-reduce

DynamiQ框架通过优化的梯度同步加速LLM训练

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

研究人员开发了DynamiQ，一个旨在通过优化梯度同步来加速大型语言模型训练的新框架。该方法通过采用新颖的部分和表示技术以及用于高效执行的融合内核，解决了大规模模型训练中的网络瓶颈问题。DynamiQ已展示出显著的改进，与现有最先进的方法相比，速度提升高达34.2%，同时保持接近基线的准确性。 AI

影响优化大型模型训练的梯度同步，可能降低计算成本和训练时间。

排序理由该集群包含一篇详细介绍加速LLM训练新框架的研究论文。[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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Newsworthiness bucket: Tool; 该集群包含一篇详细介绍加速LLM训练新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Wenchen Han, Shay Vargaftik, Michael Mitzenmacher, Ran Ben Basat · 2026-07-07 04:00

DynamiQ：使用压缩多跳全约化加速梯度同步

arXiv:2602.08923v2 Announce Type: replace Abstract: Multi-hop all-reduce is the de facto backbone of large model training. As the training scale increases, the network often becomes a bottleneck, motivating the reduction of the volume of transmitted data. Accordingly, recent syst…

本文如何被排名

报道来源 [1]

DynamiQ：使用压缩多跳全约化加速梯度同步

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