Highway networks represent a significant advancement in the training of very deep neural networks, serving as a precursor to the widely successful ResNet. By introducing gated shortcuts, highway networks enabled the flow of information across layers without degradation, allowing for the effective training of networks with unprecedented depth. This innovation addressed the vanishing gradient problem that plagued earlier deep architectures and paved the way for the development of more complex models like ResNet, which further refined these ideas to achieve superior performance in various machine learning tasks.