Unified Framework for Discrete Diffusion Models
5a-academia-attractions · hf · 2026-07-16
This paper proposes a unified framework for discrete diffusion models (DDMs), placing various approaches within the same design space: from tokenization to generation, the construction of the discrete state space dictates model behavior.
The authors note that existing methods like transition-matrix, masking/absorbing-state, and score/ratio-based approaches are essentially different instances of this framework. They further analyze the trade-offs and differences in training objectives, inference algorithms, scalability, system optimization, and evaluation protocols, concluding with several future research directions.
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