Learn from Your Mistakes: Self-Correcting Masked Diffusion Models
Masked diffusion models fail because unmasked tokens are fixed, causing errors to accumulate. The proposed Progressive Self-Correction (ProSeCo) framework trains a model to both unmask and correct mistakes. It achieves this by training a corrector alongside the denoiser and interleaving corrective refinement steps during sampling, leading to faster generation with higher quality.