Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate

Hyperparameter transfer is crucial for training large language models, as it enables the extrapolation of optimal optimization hyperparameters from small to large scales. Researchers have explored two primary approaches to achieve this: fitting a scaling law to the hyperparameters or utilizing a parameterization, such as Maximal Update, that renders optimal hyperparameters approximately scale invariant. A recent study investigated the importance of embedding layer learning rate in hyperparameter transfer, highlighting its significant impact on the performance of large language models¹. The findings suggest that careful tuning of the embedding layer learning rate is essential for effective hyperparameter transfer. This is particularly significant, as large language models are increasingly being used in various applications, and optimizing their performance is critical. The ability to transfer hyperparameters from small to large scales can substantially reduce the computational resources required for training, making it a vital area of research. So what matters to practitioners is that understanding the role of embedding layer learning rate in hyperparameter transfer can help them optimize the performance of large language models.