Learning rate warmup is a technique used in training deep neural networks which gradually increases the learning rate from a small value. It was introduced to help large models, especially when using batch normalization, to avoid large gradient updates early in training, which can be destabilizing. The rationale behind is during the early stage of training, the model parameters are randomly initialized, thus the gradients can be large and noisy. Therefore, small learning rates are preferred initially to prevent the gradients from exploding and causing training instability. As training progresses, the learning rate is increased to accelerate the training speed. There are various types of warmup such as constant learning rate warmup, linear learning rate warmup, etc., each having their own specific interpretation. The right choice depends largely on your scenario and empirical trial. There's no one-size-fits-all value for the warmup period. Depending on your model and dataset, you might want to experiment with different values. In terms of “how long”, it typically ranges from 0-10,000 training steps, with popular choices around 1,000 steps in various papers. It's also common to decay the learning rate after warmup.

Learning rate warmup is a technique used in deep learning which gradually increases the learning rate from a very small value to a larger value during the early stages of model training. The interpretation behind this is to prevent the model from converging too quickly to a sub-optimal solution. In deep learning network optimization, if the learning rate is too high at the start, the parameter updates may be too large causing the model to miss the optimal solution or oscillate around it. Conversely, if the learning rate is too low, the model might get stuck in a poor local minimum or take too long to converge. By starting with a small learning rate (warming up), the model parameters start changing slowly, allowing the model to explore the solution space more carefully. Then, as the learning rate increases, the model can use these larger steps to quickly converge to an optimal solution. This is particularly beneficial for complex models like those based on deep learning architectures. Finally, using a learning rate schedule that decreases the learning rate after the warmup period can also help the model to make smaller adjustments as it gets closer to the optimal solution, thus ensuring fine-tuning of the model's parameters.