The loss function used for training the model is typically the categorical cross-entropy loss, which measures the difference between the predicted probability distribution of the next word and the actual word in the caption. This loss function is computed for each word in the sequence, and the total loss is averaged over all words in the batch. The goal of the training process is to minimize this loss, thereby improving the accuracy of the generated captions.