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Dust: Pretraining Transformers Without Backpropagation

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Dust perturbs activations (node perturbation) independently at every token, so each token is a virtual population member and one forward pass evaluates them all in parallel. Dust approximates backprop closely at large population (i.e. substantially more compute) and in multiple settings even exceeds it. This hints that in a compute-rich regime we might be able to surpass backprop.

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Original publisherQlabs
Canonical URLhttps://qlabs.sh/research/dust
Publication timeMon, 05 Oct 2026 21:15:07 +0000
Retrieval time2026-10-05T21:55:43.698Z
Last seen2026-10-05T21:55:43.698Z
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Dust: Pretraining Transformers Without Backpropagation Samip Dahal, Bishwas Mandal, Serdar Gülbahar, Akshay Vegesna October 2026Correspondence to [email protected]·Code·Cite Copy @misc{dahal2026backprop, title = {Dust: Pretraining Transformers Without Backpropagation}, author = {Dahal, Samip and Mandal, Bishwas and G{\"u}lbahar, Serdar and Vegesna, Akshay}, year = {2026}, url = {https://qlabs.sh/research/dust} } TL;DR We present the first zeroth-order method that is competitive with backprop at pretraining transformer language models. Dust perturbs activations (node perturbation) independently at every token, so each token is a virtual population member and one forward pass evaluates them all in parallel. Dust approximates backprop closely at large population (i.e.

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