Similar to Esben et al. in their proposed Double Loop RL algorithm, increasing the number of augmentation rounds increases susceptibility to mode collapse (Figure ). We used the Aripiprazole Similarity task to perform a grid optimization and found two rounds to be optimal for stability. At three rounds, mode collapse is already observed with triplicate runs. Initial screening experiments identified two augmentation rounds to be optimal for training stability. We envisioned in pure exploitation scenarios where Selective Memory Purge is not used, mode collapse may be possible. The rationale being that the Agent is reinforced on the same replay buffer molecules. In the case where the entire replay buffer contains very similar or identical molecules, mode collapse may occur. This is not an issue when using Selective Memory Purge as entries in the replay buffer would be removed, thus preventing the entire buffer containing the same molecules. We verified this statement by performing 100 replicates of Aripiprazole Similarity using Selective Memory Purge. All replicates rediscovered Aripiprazole, indicating no mode collapse at sub-optimal minima.