Minimax Optimality of Score-Entropy Discrete Diffusion
Discrete diffusion models have demonstrated strong performance across a range of datasets, including natural language data and graph-structured data. Among many variants, score-entropy discrete diffusion (SEDD) has achieved particularly strong empirical results. In SEDD, new samples are generated by iteratively evaluating a sequence of concrete score functions, which are learned by minimizing a score-entropy loss. While much of the prior theoretical literature on discrete diffusion has focused on the sampling efficiency of SEDD under the assumption of small score estimation error, recent work has begun to investigate the finite-sample properties of score estimation itself. In this work, w...
| Authors | Cholyeon Cho, Yuchen Wu |
|---|---|
| Primary category | stat.ML |
| Published | 21 August 2026 |
| Last updated at source | 21 August 2026 |
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