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Affiliation:, University of Toronto
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A few critical denoising steps in diffusion language models dictate the entire output, and training only these "pivots" dramatically boosts performance.
Masked diffusion language models can now achieve 21.8x better compute efficiency than autoregressive models, thanks to binary encoding and index shuffling.