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Diffusion Beats Autoregressive in Data-Constrained Settings
Check out our new blog post on "Diffusion beats Autoregressive in Data-Constrained settings". The era of infinite internet data is ending. This research paper asks: What is the right generative modeling objective when data—not compute—is the bottleneck?
Numbers taken from: Sahoo etal “Simple and Effective Masked Diffusion Language Models”In the table above, we highlight representative results from a popular work.The takeaways are as follows: By adding explicit data augmentations to AR training, we find that diffusion model’s advantage arises from their exposure to a diverse set of token orderings. @article{prabhudesai2025diffusion,title={Diffusion Beats Autoregressive in Data-Constrained Settings},author={Prabhudesai, Mihir and Wu, Mengning and Zadeh, Amir and Fragkiadaki, Katerina and Pathak, Deepak},journal={arXiv preprint arXiv:2507.15857},year={2025}}
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