Warmup Steps

Warmup steps are a technique used in training machine learning models, particularly in deep learning, to gradually increase the learning rate during the initial phase of training. This approach helps stabilize the training process by preventing large updates to the model weights that can occur if a high learning rate is applied from the start. Typically, a specified number of iterations or epochs are allocated for the warmup phase, during which the learning rate is incrementally increased to the desired value. Common use cases include training neural networks for tasks such as image recognition, natural language processing, and reinforcement learning, where model convergence is critical.