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

2026-08-28

AI Summary

研究了英语语料库上预训练大规模语言模型的学习率和批量大小的缩放行为，采用Warmup-Stable-Decay学习率调度，并探讨了损失与模型容量和数据集大小之间的关系。

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 \textit{marginal} evolution with model capacity and data scale and develop a model that captures these relationships. As we employ a Warmup-Stable-Decay learning rate schedule, we further investigate the gains from learning rate annealing over a broad range of hyperparameters settings, models and data budgets, and whether the optimal learning rate and batch size \textit{transfer} between the stable and decay phases. Finally, we characterize the dependence of loss on model capacity and dataset size, evaluating recently proposed scaling forms that explicitly model their interaction. We find these approaches particularly effective at capturing both undertraining and overtraining regimes across our experiments. This study establishes a first baseline and scaling procedure for the development of future OpenEuroLLM models. We open-source the complete collection of pretraining runs used in this study.

Problem

• scaling behavior
• learning rate
• batch size
• model capacity
• data scale

Innovation

• scaling laws
• learning rate schedule
• batch size
• loss dependence
• model capacity