English(EN) How Inner Optimizers Shape Replica Scaling

研究：内部优化器对大型语言模型副本扩展起关键作用

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

一篇新的研究论文探讨了内部优化器的选择如何影响大型语言模型（LLMs）在分布式计算环境中的扩展。研究发现，伪梯度的协方差结构以及批量大小对噪声尺度影响是影响副本扩展的关键因素。研究结果表明，基于白化的预处理是内部优化器的一个有利特征，可以提高副本扩展性能，尤其是在参数和数据扩展时与差分隐私方法相比。 AI

影响确定了高效分布式LLM训练的关键因素，可能提高可扩展性。

排序理由关于机器学习优化技术的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Eugene Belilovsky, Stefan Horoi, Edouard Oyallon, Benjamin Th\'erien · 2026-10-15 04:00

内部优化器如何塑造副本扩展

arXiv:2610.19064v1 Announce Type: new Abstract: Training large language models (LLMs) across geographically distributed and poorly interconnected compute pools or data centers has become a topic of significant interest. This has led to increased study of local optimization method…

本文如何被排名

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内部优化器如何塑造副本扩展

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