For the first iteration of the REINVENT loop, we set N steps to 1200 so that the generator goes through a sufficient number of optimization steps to achieve a high balanced score between the three objectives, and therefore generate a representative initial pool U r for the first iteration of the Active Learning loop. We set λ to 180 in the REINVENT loss function to accelerate convergence to high reward scores from the multi-objective scoring function. N steps was then reduced to 250 with the purpose of fine-tuning the generation process after observing expert feedback.