English(EN) Accelerating LLM Pre-Training through Flat-Direction Dynamics Enhancement

新的LITE策略通过增强平坦方向动力学加速大语言模型预训练

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 04:00

研究人员开发了一种名为LITE的新加速策略，以提高大语言模型（LLM）预训练的效率。LITE通过在优化景观的平坦方向上应用更大的阻尼系数和学习率来增强训练动力学，这对于损失降低至关重要。该方法已被证明可以在各种模型架构、参数规模和数据集上显著加速Muon和SOAP等优化器，为更高效的大语言模型预训练提供了一种有原则的方法。 AI

影响加速大语言模型预训练，可能降低开发大型模型的计算成本和时间。

排序理由这是一篇详细介绍加速大语言模型预训练新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Shuchen Zhu, Rizhen Hu, Mingze Wang, Mou Sun, Xue Wang, Kun Yuan, Zaiwen Wen · 2026-10-28 04:00

通过平坦方向动力学增强加速LLM预训练

arXiv:2602.22681v2 Announce Type: replace Abstract: Pre-training Large Language Models requires immense computational resources, making optimizer efficiency essential. The optimization landscape is highly anisotropic, with loss reduction driven predominantly by progress along fla…

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通过平坦方向动力学增强加速LLM预训练

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