Linear and cosine beta schedules are techniques used in various machine learning and optimization processes, particularly in training neural networks. These schedules dictate how the learning rate or noise level changes over time. The linear schedule gradually decreases the beta (or learning rate) in a straight line from the beginning to the end of the training process, while the cosine schedule follows a cosine curve, offering a smoother and more gradual decline.
The effect of a linear schedule is straightforward: the beta value drops consistently at each step, leading to a steady decrease in learning rate. This can be beneficial for problems where a uniform decrease helps stabilize training, allowing the model to converge steadily. For instance, if you set a linear decay from a high learning rate to a lower one over a set number of epochs, you ensure that the model weight updates gradually slow down, helping fine-tune the parameters effectively towards the end of the training. However, a downside of the linear method is that it might not capture the nuanced changes needed in more complex learning scenarios.
On the other hand, a cosine schedule provides a more dynamic adjustment. The beta value decreases quickly at first but slows down as it approaches zero, closely mimicking a wave curve. This approach can be advantageous in circumstances where the training process benefits from abrupt learning rate changes at specific points, as the model can explore the loss landscape more thoroughly early on and then fine-tune towards convergence later. For example, in tasks involving generative models or reinforcement learning, a cosine schedule allows for more exploration initially, which can lead to better solutions. Overall, the choice between these two schedules often depends on the specific task, model, and desired convergence behavior.