Warmup

Warmup is a technique used in training neural networks where the learning rate is gradually increased from a small initial value to the target learning rate over a predefined number of iterations or epochs. This approach helps to stabilize the training process, particularly in deep learning models, by preventing large updates that could lead to divergence or instability in the early stages of training. The warmup phase can be mathematically represented as a linear or exponential increase in the learning rate, allowing the model to adapt more smoothly to the optimization landscape. This technique is often employed in conjunction with learning rate schedules and has been shown to improve convergence rates and final model performance, especially in complex architectures.