Abstract
Diffusion large language models (dLLMs) have emerged as a promising alternative for text generation, distinguished by their native support for parallel decoding. In practice, block inference is crucial for avoiding order misalignment in global bidirectional decoding and improving output quality. However, the widely-used fixed, predefined block (naive) schedule is agnostic to semantic difficulty, making it a suboptimal strategy for both quality and efficiency: it can force premature commitments to uncertain positions while delaying easy positions near block boundaries. In this work, we analyze the limitations of naive block scheduling and disclose the importance of dynamically adapting the schedule to semantic difficulty for reliable and efficient inference. Motivated by this, we propose Dynamic Sliding Block (DSB), a training-free block scheduling method that uses a sliding block with a dynamic size to overcome the rigidity of the naive block. To further improve efficiency, we introduce DSB Cache, a training-free KV-cache mechanism tailored to DSB. Extensive experiments across multiple models and benchmarks demonstrate that DSB, together with DSB Cache, consistently improves both generation quality and inference efficiency for dLLMs. Code is released at this https URL.
Abstract (translated)
扩散型大型语言模型（dLLM）作为一种有前景的文本生成替代方案已经崭露头角，其特点是原生支持并行解码。实践中，块推理对于避免全局双向解码中的顺序错位以及提高输出质量至关重要。然而，广泛使用的固定、预定义块（朴素）调度方法不考虑语义难度，这使得它在质量和效率方面都不是最优策略：可能会强制过早地对不确定位置做出承诺，同时推迟接近区块边界处容易解决的位置。 在这项工作中，我们分析了朴素块调度的局限性，并揭示了根据语义难度动态调整调度表对于可靠且高效推理的重要性。受此启发，我们提出了动态滑动块（DSB），这是一种无训练的块调度方法，使用具有动态大小的滑动块来克服朴素块的刚性。为了进一步提高效率，我们引入了针对DSB定制的无需训练的KV缓存机制——DSB Cache。 在多个模型和基准上的广泛实验表明，无论是生成质量还是推理效率，DSB（连同DSB Cache）都能持续提升dLLM的表现。相关代码已在此网址发布：[此URL]。
URL
https://arxiv.org/abs/2602.05992
