The insights from the success of GvT on small datasets can be applied to improve the performance of vision transformers on large-scale datasets by focusing on several key areas. Firstly, the inductive bias introduced by the graph convolutional projection in GvT can be leveraged to capture long-range dependencies and spatial relationships in larger datasets. By scaling up the model architecture and incorporating more complex graph structures, vision transformers can better handle the increased data volume and diversity present in large-scale datasets. Additionally, the lessons learned from addressing the low-rank bottleneck in attention heads using talking-heads technology can be applied to enhance the scalability and efficiency of vision transformers on larger datasets. By optimizing the model architecture and training strategies based on the insights gained from GvT, vision transformers can achieve superior performance on large-scale datasets without the need for extensive pre-training.