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This paper introduces Simplax, an exact Dirichlet–categorical augmentation for discrete diffusion models that enriches training objectives and reverse transitions while preserving the original categorical corruption process, improving perplexity–entropy tradeoff on OpenWebText and validity on Sudoku.
Introduces Simplax, an exact Dirichlet-categorical augmentation for uniform discrete diffusion that improves reverse sampling and generative quality on text and Sudoku tasks.