Introduces adjoint-equation framework establishing dimension-free convergence bounds in any IPM for discrete diffusion models under masked and uniform priors.
Advances in neural information processing systems , volume=
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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
Introduces adjoint-equation framework establishing dimension-free convergence bounds in any IPM for discrete diffusion models under masked and uniform priors.
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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.