The SMILES grammar is an interpretable string notation of the 2D graph traversal of a molecule (and can include stereochemistry). Therefore, a CLM trained on the SMILES grammar can conduct conditional (a.k.a. constrained from a chemistry perspective) generation by completion of a SMILES prompt, for example, a benzene prompt ("[GO]c1ccccc1") could be continued into benzanoic acid ("[GO]c1ccccc1C(=O)[EOS]"). However, there are two obvious limitations to this from the chemistry perspective 1) string extension means that atoms are added to the last atom in the SMILES string and therefore the last atom must be the desired attachment point, and 2) string generation is unidirectional, atoms can only be added to one single attachment point. Conveniently, a single molecule has multiple SMILES representations that can be controlled depending on the starting point and algorithm used for graph traversal. Therefore, SMILES can be generated such that the attachment point of interest can be re-arranged to be the last atom in the SMILES string. Given this simple observation, prompt-based SMILES generation can be repeated for multiple attachment points. This also results in conditional generation based on previously extended attachment points. However, this iterative approach to prompted SMILES generation introduces a new limitation: token generation continues until a stop token has a high probability and is therefore sampled, i.e., the model is likely to consider the molecule complete after the first iteration. Therefore, we propose that RL will fine-tune the model to adapt to the new task of iterative prompt-based generation.