Different Rates for Different Weights: Decoupled Relative Learning Rate Schedules

Jan Ludziejewski, Jan Małaśnicki, Maciej Pióro, Michał Krutul, Kamil Ciebiera, Maciej Stefaniak, Jakub Krajewski, Piotr Sankowski, Marek Cygan, Kamil Adamczewski, Sebastian Jaszczur

Keywords: learning rate, transformer, mixture of experts, LLM

Abstract:

In this work, we introduce a novel approach for optimizing neural network training by adjusting learning rates across weights of different components in Transformer models. Traditional methods often apply a uniform learning rate across all network layers, potentially overlooking the unique dynamics of each part. Remarkably, our introduced Relative Learning Rate Schedules (RLRS) method accelerates the training process by 13.6%, particularly in complex models such as the Mixture of Experts (MoE). Hyperparameters of RLRS can be efficiently tuned on smaller models and then extrapolated to 27x larger ones. This simple and effective method results in a substantial reduction in training time and computational resources, offering a practical and scalable solution for optimizing large-scale neural networks.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 3807