A learning rate schedule is a strategy used in training neural networks to adjust the learning rate over time. The learning rate is a crucial hyperparameter that determines how much to change the model in response to the estimated error each time the model weights are updated. Properly setting and varying the learning rate can significantly impact the performance and convergence of the model.

One common approach is to reduce the learning rate as training progresses. This is based on the intuition that a high learning rate can help the model converge quickly in the early stages, while a lower learning rate can refine the model’s performance in later stages. For instance, a cosine learning rate schedule starts with a higher learning rate and gradually decreases it, allowing for fine-tuning towards the end of training ¹³.

Different learning rate schedules can be implemented, such as step decay, exponential decay, and warmup strategies, each with its own advantages depending on the specific training scenario ²⁴⁵. These schedules help in achieving better training efficiency and model performance.