Simple Self-Conditioning Adaptation for Masked Diffusion Models
Masked diffusion models (MDMs) generate discrete sequences by iterative denoising under an absorbing masking process. In standard masked diffusion, if a token remains masked after a reverse update, the model discards its clean-state prediction for that position. Thus, still-masked positions must be repeatedly inferred from the mask token alone. This design choice limits cross-step refinement. To address this limitation, this paper proposes a simple, yet effective, post-training adaptation for MDMs that conditions each denoising step on the model's own previous clean-state predictions. The resulting method, called Self-Conditioned Masked Diffusion Models (SCMDM), requires minimal architect...
| Authors | Michael Cardei, Huu Binh Ta, Ferdinando Fioretto |
|---|---|
| Primary category | cs.LG |
| Published | 28 April 2026 |
| Last updated at source | 6 June 2026 |
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