We train SMILESLM with minibatch stochastic gradient descend for parameter updates and backpropagation for gradient calculations. Since we process long sequences, we try to prevent gradient explosion by applying gradient clipping. To reduce overfitting, we employ dropout between hidden layers in the encoder, set up a learning rate scheduler, and monitor the progress on the generated data with early stopping.