Here, we evaluate the effect of the priors trained on different sizes of training data, in particular, the transformer model trained on ChEMBL and Pub-Chem . Figure shows the number of unique scaffolds with P(active)>0.6 and QED>0.6 for scaffold discovery task. Without RL, the PubChem prior already yields more compounds of interest than the ChEMBL prior. This could be because the PubChem prior was trained on a much larger scale (200B vs 6.5M). Most RL configurations improve performance for both priors. The PubChem prior consistently outperforms the ChEMBL prior with the exception of RL DF(scaffold) where ChEMBL prior show comparable performance. This might be because the Pub-Chem prior has a knowledge of closer area of an input molecule than ChEMBL prior, resulting in a slower adaptation of diverse scaffolds generation. Figure shows the results for molecular optimization task. Similarly, the PubChem prior generates more compounds with desirable properties compared to the ChEMBL prior. In general, RL facilitates the generation of more compounds with desirable properties for both priors, with the PubChem prior typically outperforming the ChEMBL prior in the evaluated tasks.