OpenEuroLLM团队开源了预训练研究，揭示了学习率和批量大小如何随模型和数据规模变化。
该研究分析了英语语料上预训练密集大语言模型的学习率和批量大小缩放行为。研究开发了捕捉学习率和批量大小与模型容量和数据规模关系的模型。研究评估了最近提出的缩放形式，发现它们能有效捕捉实验中的欠训练和过训练状态。研究建立了OpenEuroLLM模型开发的基准和缩放程序。
Deriving Scaling Laws for OpenEuroLLM Models: Learning Rate, Batch Size and Loss
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.