Learning rate warmup is a strategy employed in the training phase of deep learning models, specifically during the optimization process. In simple terms, it is a schedule for the learning rate such that initial iterations start with a lower, gradually increasing learning rate, before reaching the maximum learning rate used for the rest of the training. The main idea behind this approach is that in the beginning stages of training, our model's weights are initialized randomly and thus the loss surface is barely understood. A too high learning rate at this stage can make the model diverge or converge too fast to an unsuitable local minimum. By gradually increasing the learning rate, we allow the model to make smaller updates in the beginning, effectively reducing the risk of instability. After the warmup period, typically the learning rate will decrease over time or epochs according to a pre-defined schedule like step decay, cosine annealing or similar methods. So as a summary, in the context of deep learning, learning rate warmup is a practice aimed at making the training process more stable and efficient, which results in effective and more robust models. For someone stumbling across this question in the future, take note that implementing learning rate warmup can have varying results depending on the type and complexity of your models and the data they're trained on. It's not a mandatory approach, rather it's a tool among many in your deep learning toolbox. Test it in your own projects, tweaking and observing as necessary.