The PyTorch training loop involves several key components, including the data pipeline, model, optimizer, and scheduler, which must be carefully configured to achieve optimal results. Common mistakes to watch out for include incorrect data loading, model initialization, and optimizer configuration, as well as failure to properly switch between training and evaluation modes.