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[2602.20126] Adaptation to Intrinsic Dependence in Diffusion Language Models
Summary
This article presents a novel unmasking schedule for diffusion language models (DLMs) that adapts to the intrinsic dependence of data distributions, enhancing sampling efficiency without prior knowledge or hyperparameter tuning.
Why It Matters
Understanding how DLMs can adapt to data structures is crucial for improving generative models in machine learning. This research provides a theoretical foundation for optimizing sampling processes, which can lead to more efficient and effective AI applications in natural language processing and beyond.
Key Takeaways
- Introduces a distribution-agnostic unmasking schedule for DLMs.
- Randomizes token revelation to enhance sampling efficiency.
- Demonstrates improved convergence guarantees compared to previous methods.
- Applicable in parallel-sampling regimes, crucial for real-time applications.
- Sheds light on the importance of intrinsic data structures in model design.
Computer Science > Machine Learning arXiv:2602.20126 (cs) [Submitted on 23 Feb 2026] Title:Adaptation to Intrinsic Dependence in Diffusion Language Models Authors:Yunxiao Zhao, Changxiao Cai View a PDF of the paper titled Adaptation to Intrinsic Dependence in Diffusion Language Models, by Yunxiao Zhao and 1 other authors View PDF HTML (experimental) Abstract:Diffusion language models (DLMs) have recently emerged as a promising alternative to autoregressive (AR) approaches, enabling parallel token generation beyond a rigid left-to-right order. Despite growing empirical success, the theoretical understanding of how unmasking schedules -- which specify the order and size of unmasked tokens during sampling -- affect generation quality remains limited. In this work, we introduce a distribution-agnostic unmasking schedule for DLMs that adapts to the (unknown) dependence structure of the target data distribution, without requiring any prior knowledge or hyperparameter tuning. In contrast to prior deterministic procedures that fix unmasking sizes, our method randomizes the number of tokens revealed at each iteration. We show that, for two specific parameter choices, the sampling convergence guarantees -- measured by Kullback-Leibler (KL) divergence -- scale as $\widetilde O(\mathsf{TC}/K)$ and $\widetilde O(\mathsf{DTC}/K)$ respectively. Here, $K$ is the number of iterations, and $\mathsf{TC}$ and $\mathsf{DTC}$ are the total correlation and dual total correlation of the target di...