Across all three finetuning datasets, we see consistent performance increase, solidifying our expectation that teaching the base model ORCA input synthesis is essential due to its limited pre-trained knowledge. Importantly, these improvements are achieved with only 500 synthetic ORCA input files, highlighting the large potential of synthetic datasets to enhance real-world DSL synthesis performance. Overall, the manual-based models perform best, likely because the corresponding training data in- This is in marked contrast to the brute-force dataset, which shows lower F1 scores. We argue that this is because many of the randomly selected keywords and input blocks are not used in real-world calculations. Interestingly, the brute-force approach was able to generate more executable input files compared to the other datasets. This is surprising as this approach combines documentation in the most random way. However, as all our finetuning datasets only included executable files, the specific combinations of keywords, settings, and options do not need to be meaningful for that matter.