We first present results for molecule language models with minimal alignment fine-tuning, initialising pretrained weights from molecule generation rather than deploying model merging (see Appx. G for details). Tables and summarise experimental results. Generally, benchmarking models trained on extensive data with SFT exhibit memorisation effects, with performance dropping by 50% to 100% compared to reported results, when evaluated on out-of-distribution data.