Adjoint-equation framework yields dimension-free convergence bounds in any IPM for discrete diffusion models under masked or uniform priors using one rate-matrix regularity assumption.
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Discrete diffusion models learn data support before frequencies because the exact reverse process decomposes edits into a dominant validity scale and a finer probability coefficient.
NI Sampling accelerates discrete diffusion language models up to 14.3 times by training a neural indicator to select which tokens to sample at each step using a trajectory-preserving objective.
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Dimension-Free Convergence of Discrete Diffusion Models: Adjoint Equations Induce the Right Space
Adjoint-equation framework yields dimension-free convergence bounds in any IPM for discrete diffusion models under masked or uniform priors using one rate-matrix regularity assumption.
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Support Before Frequency in Discrete Diffusion
Discrete diffusion models learn data support before frequencies because the exact reverse process decomposes edits into a dominant validity scale and a finer probability coefficient.
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NI Sampling: Accelerating Discrete Diffusion Sampling by Token Order Optimization
NI Sampling accelerates discrete diffusion language models up to 14.3 times by training a neural indicator to select which tokens to sample at each step using a trajectory-preserving objective.