As the depth and width of the network grows, the computational advantage grows. Forward only learning algorithms, such as FF and PFF, have this advantage.

There is also a compatability advantage: forward only learning algorithm work on limited resource devices (edge devices) and neuromorphic chips.

For an analysis on efficiency, refer to section IV: https://arxiv.org/abs/2204.01723

We demonstrate reasonable performance on cifar-10 and cifar-100, in that same paper (Section IV). So, the performance gap may decrease over time.

For a review of forward only learning, with an explaination on why it has efficiency and compatibility: https://amassivek.github.io/sigprop