What is the role of layer boundaries in transformer training?

Layer boundaries in transformers play a crucial role in defining the structural hierarchy of the model. Each layer has its own set of parameters and activations, which are transformed through operations like attention and feed-forward networks. During training, these boundaries ensure that gradients are properly calculated and backpropagated, allowing each layer to learn distinct features of the data. This modular approach helps in fine-tuning specific layers during transfer learning and also facilitates the addition of new layers to adapt the model to different tasks.