Interpolating Discrete Diffusion Models with Controllable Resampling
Discrete diffusion models form a powerful class of generative models across diverse domains, including text and graphs. However, existing approaches face fundamental limitations. Masked diffusion models suffer from irreversible errors due to early unmasking, while uniform diffusion models, despite enabling self-correction, often yield low-quality samples due to their strong reliance on intermediate latent states. We introduce IDDM, an Interpolating Discrete Diffusion Model, that improves diffusion by reducing dependence on intermediate latent states. Central to IDDM is a controllable resampling mechanism that partially resets probability mass to the marginal distribution, mitigating error...
| Authors | Marcel Kollovieh, Sirine Ayadi, Stephan Günnemann |
|---|---|
| Primary category | cs.LG |
| Published | 19 April 2026 |
| Last updated at source | 19 April 2026 |
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