It is worth noting that during training, the iteration step r is uniformly sampled between 1 and R, where R is the maximum number of iterations (for this stage, R = R 1 ). Before iteration r, the model operates only in forward mode, without computing the loss or performing backpropagation. Finally, at iteration r, gradient computation and backpropagation are enabled. During inference, iterative sampling is not used.