<ul><li>Training large language models (LLMs) faces challenges due to their scale and complex architectures.</li><li>An optimizer wrapper called Scaling with Gradient Grouping (SGG) is introduced to improve adaptive learning rate estimation.</li><li>SGG groups gradient statistics in each layer, applies cluster-specific scaling, and calibrates learning rates for each parameter, enhancing learning rate estimation.</li><li>Experiments indicate that SGG seamlessly integrates with existing optimizers, offers consistent gains, faster convergence, and stability across various batch sizes and learning rates for LLM optimization.</li></ul>

Taming LLMs by Scaling Learning Rates with Gradient Grouping

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