REINVENT Curriculum Learning Extension. The implementation of CL builds on the REINVENT generative model, which uses a recurrent neural network (RNN) architecture. The molecular design task is formulated as a natural language processing (NLP) problem where compounds are sampled in the SMILES format based on conditional probabilities. The RNN in this work features three hidden layers of 512 long short-term memory (LSTM) cells with an embedding size of 256 and a linear layer with softmax activation. A prior generative model is first trained on the ChEMBL dataset to learn the SMILES syntax. The agent is initialized as the prior and is then focused towards a MPO task via RL.