Our results indicate that delta learning is the most effective approach for leveraging low-level data to enhance the accuracy of activation energy predictions. By directly incorporating low-level activation energies or predicting the difference between lowand high-level energies, delta learning demonstrated superior performance in reducing prediction errors and required substantially less high-level data to achieve competitive performance. Specifically, delta learning trained with only 20%-30% of high-level data achieved accuracy comparable to, or exceeding, that of other data augmentation methods trained with the full dataset, making it particularly advantageous in data-scarce scenarios. However, this method also imposes significant computational demands, particularly due to the need for transition state structure searches during model application, which are both complex and resource-intensive.