We commenced our study by comparing the performance of different transfer learning approaches, focusing on methods previously used for chemical reaction prediction (i.e., multitask learning and fine-tuning) and methods employed in the NLP domain (mixed fine-tuning and ensemble decoding) (Figure ). The comparison is conducted on the Ring test set to assess their performance in predicting ring-breaking reactions compared to a baseline model trained only on the General dataset (Figure ). In addition to the commonly used reactantaccuracy, we also consider whether the prediction was chemically valid and corresponded to a ring-breaking reaction. This identifies predictions that differ from the ground truth disconnection present in the test set but still disconnect the ring.