title: Remasking Discrete Diffusion Models with Inference-Time Scaling
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 is derived from a discrete diffusion model
  with a custom remasking backward process. Most interestingly, ReMDM endows
  discrete diffusion with a form of inference-time compute scaling. By increasing
  the number of sampling steps, ReMDM generates natural language outputs that
  approach the quality of autoregressive models, whereas when the computation
  budget is limited, ReMDM better maintains quality. ReMDM also improves sample
  quality of masked diffusion models for discretized images, and in scientific
  domains such as molecule design, ReMDM facilitates diffusion guidance and
  pushes the Pareto frontier of controllability relative to classical masking and
  uniform noise diffusion. We provide the code along with a blog post on the
  project page: https://remdm.github.io.
paper_authors._id: 67c9fcfc765ba582a758e083
paper_authors.name: Guanghan Wang
paper_authors._id: 67c9fcfc765ba582a758e084
paper_authors.name: Yair Schiff
paper_authors._id: 67c9fcfc765ba582a758e085
paper_authors.name: Subham Sekhar Sahoo
paper_authors._id: 67c9fcfc765ba582a758e086
paper_authors.name: Volodymyr Kuleshov
paper_title: Remasking Discrete Diffusion Models with Inference-Time Scaling
paper_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 is derived from a discrete diffusion model
  with a custom remasking backward process. Most interestingly, ReMDM endows
  discrete diffusion with a form of inference-time compute scaling. By increasing
  the number of sampling steps, ReMDM generates natural language outputs that
  approach the quality of autoregressive models, whereas when the computation
  budget is limited, ReMDM better maintains quality. ReMDM also improves sample
  quality of masked diffusion models for discretized images, and in scientific
  domains such as molecule design, ReMDM facilitates diffusion guidance and
  pushes the Pareto frontier of controllability relative to classical masking and
  uniform noise diffusion. We provide the code along with a blog post on the
  project page: https://remdm.github.io.
paper_ai_keywords: diffusion models iterative refinement masked discrete diffusion remasking diffusion model (ReMDM) sampling steps natural language outputs autoregressive models sample quality discretized images molecule design diffusion guidance Pareto frontier