An optimization algorithm, such as Adam or RMSprop, is employed to update the model parameters based on the computed gradients from the loss function. The learning rate is a critical hyperparameter that determines the step size during the optimization process. A learning rate schedule may be implemented to adjust the learning rate dynamically during training, allowing for faster convergence and better performance.