Attention-Based Sampler for Diffusion Language Models
Auto-regressive models (ARMs) have established a dominant paradigm in language modeling. However, their strictly sequential sampling paradigm imposes fundamental constraints on both inference efficiency and modeling flexibility. To address these limitations, diffusion-based large language models (dLLMs) have been proposed, offering the potential for parallel sampling and flexible language modeling. Despite these advantages, current dLLMs sampling strategies rely primarily on token level information, which fails to account for global sequence structure and often yields suboptimal results. In this paper, we study the sampling order selection problem from the perspective of log-likelihood ma...
| Authors | Yuyan Zhou, Kai Syun Hou, Weiyu Chen et al. |
|---|---|
| Primary category | cs.CL |
| Published | 18 March 2026 |
| Last updated at source | 3 June 2026 |
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