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.