Decoupled Relative Learning Rate Schedules

• URL: http://arxiv.org/abs/2507.03526v1
• Date: Fri, 04 Jul 2025 12:23:45 GMT
• Title: Decoupled Relative Learning Rate Schedules
• Authors: Jan Ludziejewski, Jan Małaśnicki, Maciej Pióro, Michał Krutul, Kamil Ciebiera, Maciej Stefaniak, Jakub Krajewski, Piotr Sankowski, Marek Cygan, Kamil Adamczewski, Sebastian Jaszczur,
• Abstract summary: We introduce a novel approach for optimizing LLM training by adjusting learning rates across weights of different components in Transformer models.<n>Our introduced relative learning rates, RLRS, method accelerates the training process by up to $23%$.
• Score: 4.34286535607654
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we introduce a novel approach for optimizing LLM 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 rates, RLRS, method accelerates the training process by up to $23\%$, particularly in complex models such as Mixture of Experts (MoE). Hyperparameters of RLRS can be efficiently tuned on smaller models and then effectively reused on models up to $27\times$ larger. 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.

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