We design a pretraining-assisted multi-task learning paradigm for the HGAR model. The Graphormers in the HGAR model are pretrained on PCQM4M-LSC , and the pretraining task is predicting the DFT-calculated HOMO-LUMO energy gap of molecules given their 2D molecular graphs. Plugging the pretrained Graphormers into the model, we further train the model in a multi-task learning paradigm, in which two atom-level tasks (i.e., reaction center prediction and bond type prediction) and two motif-level tasks (i.e., motif prediction and topological completeness prediction) are considered jointly. Such a two-step learning method suppresses the over-fitting risk, leading to a model with better generalization power.