Optimizing recurrent networks involves effective initialization techniques like Xavier and He initialization to maintain weight variance. Implementing learning rate schedulers in PyTorch, such as StepLR, enhances convergence. Regularization methods, batch normalization, and GPU utilization are crucial for improving model performance and training speed.