新研究概述了 OpenEuroLLM 模型的扩展定律

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-31 04:00

一篇新发表在 arXiv 上的研究论文详细介绍了 OpenEuroLLM 模型扩展定律的推导，重点关注学习率、批次大小和损失。该研究调查了这些参数如何随着模型容量和数据规模的变化而演变，并提出了一个捕捉这些关系的模型。它还考察了学习率衰减的好处以及最优学习率在计划不同阶段之间的可转移性。该研究为开发未来的 OpenEuroLLM 模型建立了一个基准和程序，并公开了预训练运行数据。 AI

影响为开发未来的大型语言模型奠定了基础，并提供了开源预训练数据。

排序理由该集群包含一篇详细介绍 LLM 扩展定律的研究论文。[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) · Niccol\`o Ajroldi, Diana Alexandra Onutu, Haider Al-Tahan, J\"org Franke, Sampo Pyysalo, Jenia Jitsev, Aaron Klein · 2026-08-31 04:00

Deriving Scaling Laws for OpenEuroLLM Models: Learning Rate, Batch Size and Loss

arXiv:2608.28308v1 Announce Type: new Abstract: We study the scaling behavior of learning rate and batch size in pretraining dense large language models on English-prevalent corpora. Beyond scaling \textit{jointly optimal} learning rates and batch sizes, we investigate their \tex…

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Deriving Scaling Laws for OpenEuroLLM Models: Learning Rate, Batch Size and Loss

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