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Remasking Discrete Diffusion Models with Inference-Time Scaling paper by Guanghan Wang
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Paper

Remasking Discrete Diffusion Models with Inference-Time Scaling

by Guanghan Wang arxiv/2503.00307
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32.0
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P: Popularity 0
R: Recency 54
Q: Quality 60
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Part of the success of diffusion models stems from their ability to perform iterative refinement, i.e., repeatedly correcting outputs during generation. However, modern masked discrete diffusion lacks this capability: when a token is generated, it cannot be updated again, even when it introduces an error. Here, we address this limitation by introducing the remasking diffusion model (ReMDM) sampler, a method that can be applied to pretrained masked diffusion models in a principled way and that...

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Registry ID 2503.00307
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BibTeX 
@misc{arxiv_2503_00307,
  author = {Guanghan Wang},
  title = {Remasking Discrete Diffusion Models with Inference-Time Scaling Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/2503.00307}},
  note = {Accessed via Free2AITools.}
}
APA Style
Guanghan Wang. (2026). Remasking Discrete Diffusion Models with Inference-Time Scaling [Paper]. Free2AITools. https://arxiv.org/abs/2503.00307

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Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 0
Popularity (P) 0
Recency (R) 54
Quality (Q) 60

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FNI V2.0 for Remasking Discrete Diffusion Models with Inference-Time Scaling: Authority (A:0), Popularity (P:0), Recency (R:54), Quality (Q:60). Semantic (S) is a query-time baseline scored live at search.

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πŸ“ Executive Summary

"Part of the success of diffusion models stems from their ability to perform iterative refinement, i.e., repeatedly correcting outputs during generation. However, modern masked discrete diffusion lacks this capability: when a token is generated, it cannot be updated again, even when it introduces an error. Here, we address this limitation by introducing the remasking diffusion model (ReMDM) sampler, a method that can be applied to pretrained masked diffusion models in a principled way and that..."

❝ Cite Node 

@article{Wang2026Remasking,
  title={Remasking Discrete Diffusion Models with Inference-Time Scaling},
  author={Guanghan Wang},
  journal={arXiv preprint arXiv:2503.00307},
  year={2026}
}

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Guanghan Wang

πŸ”— Full Paper

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πŸ“…1970Published
⏱️54RecencyFNI pillar
βœ…60QualityFNI pillar
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id
2503.00307
slug
2503.00307
source
arxiv
author
Guanghan Wang
license
arXiv
tags
arxiv:cs.LG, arxiv:stat.ML, diffusion

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