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October 13, 2025Open Access

Remasking Discrete Diffusion Models with Inference-Time Scaling

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ReMDM enhances discrete diffusion models by allowing for iterative refinement during output generation.
By increasing sampling steps, ReMDM achieves nearly autoregressive output quality at a lower computing expense.
This method improves sample quality for both natural language outputs and image generation in discretized formats.
ReMDM facilitates enhanced controllability in molecular design, pushing boundaries compared to classical approaches.

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A novel remasking diffusion model improves sample quality in natural language and molecule design, indicating benefits over traditional methods.

Wang et al. (2025) studied this question.

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A novel remasking diffusion model improves sample quality in natural language and molecule design, indicating benefits over traditional methods.