Learn from Your Mistakes: Self-Correcting Masked Diffusion Models

This post discusses the limitations of masked diffusion models (MDMs) in generating sequences and introduces a new framework called Progressive Self-Correction (ProSeCo) that allows for iterative refinement of outputs. ProSeCo helps to improve the quality and efficiency of samples generated, outperforming standard MDMs in terms of speed and accuracy. The post is backed by experimental validation across various tasks and offers insights into model training and error correction.