Other RL methods include using a policy network to choose favourable actions in a genetic algorithm and learning a value function instead of a policy . While all methods can generate valid molecules and the policy can be fine-tuned via RL, none of the previous methods jointly address sample efficiency and a reliable mechanism to mitigate mode collapse. We note that GFlowNets 33 by construction can achieve diverse sampling but are not as sample efficient as demonstrated in the PMO benchmark . By contrast, SMILES-based models, particularly REINVENT , have been shown to be amongst the most sample efficient molecular generative models, even when compared to the newest proposed models . Moreover, their ability to learn complex molecular distributions and satisfy multi-parameter optimization (MPO) objectives has been shown in diverse benchmarks, such as GuacaMol , MOSES , and PMO . Our proposed Augmented Memory algorithm builds on this observation and exploits the non-injective nature of SMILES.