Backpropagation in an RNN (Recurrent Neural Network) works by propagating the error or loss information backward through time. It calculates the gradients of the model parameters with respect to the loss function, allowing the model to learn and adjust its weights.

During the forward pass, the RNN processes the input sequence step by step, updating its hidden state and producing an output at each step. The predicted output is then compared to the desired output using a loss function, which quantifies the model's performance.

In the backward pass, the gradients are calculated by starting from the last time step and working backward. The gradients are backpropagated through each time step, considering both the error at the current time step and the gradients from future time steps. This allows the model to learn from the entire sequence and adjust its weights accordingly.

By iteratively updating the weights based on the calculated gradients, the RNN gradually improves its ability to make accurate predictions or generate outputs for sequential data.