Remasking Discrete Diffusion Models with Inference-Time Scaling

Understand

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.