Figure plots are interpreted as follows: after epoch 1, the sampled set LL distribution has the lowest average (higher probability) and the other two sets are extremely similar and have a higher LL (lower probability). This means that the model isn't completely trained, as the SMILES strings sampled are only a subset of the ones in the training set. Between epochs 25 to 50, the distributions become more similar, and around epochs 50-100 the three plots match as much as possible, as can be seen both in (a) and in (b). When all the plots are similar it is equally probable to sample a SMILES from the training set as it is a SMILES outside it, implying that a higher percent of the database can be sampled. After this, the training set LL distribution becomes more similar to the sampled set while the validation set has higher LL. This indicates overtraining, as the model will sample a molecule from the training set with a higher probability than a molecule from the validation set. This trend becomes more pronounced in later epochs. To further understand this method, 2 billion SMILES strings were sampled every 5 epochs (totaling 80 billion). As can be seen in Figure , the total percent of generated molecules including repeats that are part of GDB-13 always increases, but in Figure decreases steadily. This is correlated with the model not being uniform, and this trend is further pronounced after epoch 100, as the molecules from the training set are generated more often.