The use of block-wise supervision in the DNA family models significantly impacts scalability compared to traditional NAS approaches. By modularizing the search space into blocks with small search spaces, the DNA models can rate all architecture candidates, as opposed to previous methods that could only explore a subset of the search space using heuristic algorithms. This approach allows for more efficient training and evaluation of architectures within each block, leading to improved scalability in searching for optimal neural architectures. Additionally, by dividing the supernet into blocks and training each block separately, DNA models can handle larger search spaces while maintaining effectiveness and efficiency.