The RL algorithm used in this work is the same as in REINVENT for comparative reasons. Specifically, we set σ = 120 as per the default in more recent versions of REINVENT . Note the implementation of this approach requires iterative sampling of the CLM and several forward passes of the network. Therefore, to ensure stable learning, RL is split into a collection phase without computing gradients, followed by an update phase given a completed SMILES string. Furthermore, we investigate two approaches to the frequency of network updates, one update per final SMILES string completion, or one for every iteration of prompt-based completion (multi). In the case of scaffold decoration, the completed SMILES string is used for the single update. For fragment linking, the initial fragment plus de novo linker SMILES is used for the single update, as using the completed SMILES would encourage the CLM to generate the fragments on its own accord. In all cases, the reward is calculated based on the completed SMILES.