PyTorch uses automatic differentiation to handle backpropagation. It keeps track of the operations performed on tensors during the forward pass and then calculates the gradients of the loss function with respect to the tensors using the chain rule during the backward pass.

PyTorch leverages dynamic computational graphs to enable efficient backpropagation. By building the computational graph on-the-fly, PyTorch can handle complex and dynamic network architectures more flexibly than static frameworks.

In PyTorch, backpropagation is efficiently implemented using a technique called reverse-mode automatic differentiation (AD). This technique allows the gradients of the loss function to be computed by traversing the computational graph backwards, starting from the output layer.