Learning Rate Scheduling

Learning rate scheduling involves adjusting the step size of the optimization algorithm during the training process to improve convergence. A high learning rate may allow for fast initial progress but can lead to oscillations around the minimum.

Conversely, a low learning rate ensures stability but may result in extremely slow training times. In the context of quantitative finance, scheduling allows the model to make large jumps when it is far from the optimal solution and smaller, more precise adjustments as it nears convergence.

This is vital when training models on noisy crypto market data where the optimal pricing parameters may shift rapidly. Common techniques include decaying the learning rate over time or using cyclic schedules to maintain exploration.

Proper scheduling prevents the model from settling into poor local optima during the training phase. It is an essential component for fine-tuning deep learning models that handle complex derivatives pricing.