Contrary to the earlier results on the pre-trained model, it is found that the RP-solv feature does not improve the fine-tuned model. Upon closer examination, we observed that the fine-tuned model with the RP-solv feature has lower training and validation loss than the model without the feature, but has higher error on both pre-training and experimental test sets. The discrepancy in performance suggests that the model overfits to the RP-solv feature during fine-tuning. The fine-tuning set contains only three classes of reactions, which are more common reactions but are less diverse than the pre-training set. It appears that the information learned about the RP-solv feature during fine-tuning does not generalize well to other reaction classes. In contrast, the fine-tuned model without the feature performs better by avoiding overfitting and also benefits from a reduced computational cost as it no longer requires the RP-solv features that need to be calculated for each reaction-solvent pair.