扩散LLM的并行解码效率一直是痛点，这篇工作直接戳中计算冗余的核心，做模型推理加速或长上下文应用的开发者值得关注，压缩方法可以直接集成到现有dLLM中。
扩散语言模型（dLLM）通过联合去噪一批[MASK]令牌实现并行解码，但大块掩码令牌导致大量计算冗余。研究发现，许多计算花费在重复处理前文上下文和特征表示相同的[MASK]令牌上。为此，提出位置保持的[MASK]令牌压缩和终端感知增强方法，通过压缩冗余计算加速解码，并自然扩展到长上下文场景。在LLaDA-8B-Instruct和LLaDA-1.5等全序列dLLM上验证了效果，对LLaDA2.0-mini等块dLLM，通过保护终端[MASK]令牌增强上下文，以极小开销提升生成质量。
Elastic-dLLM: Position Preserving Context Compression and Augmentation of Diffusion LLMs
Unlike autoregressive models, which generate one token at a time, dLLMs denoise a chunk of [MASK] tokens jointly and sample one or more tokens per step; despite enabling parallel decoding, this process incurs substantial computational cost due to the large chunk size of masked tokens. We observe that much of this cost is spent on repeatedly processing the preceding context and many [MASK] tokens with the same feature representations, indicating considerable computational redundancy. In this work, we revisit dLLM's redundancy from the perspective of [MASK] tokens. Through systematic analysis, we verify the redundancy of [MASK] tokens while revealing their critical role in providing structural information. Guided by these findings, we propose position-preserving [MASK] token compression and terminal-aware augmentation. By compressing redundant [MASK] computation, this approach accelerates decoding and further provides a natural extension toward context-folding-like long-context scaling under limited input-length constraints for full-sequence dLLMs such as LLaDA-8B-Instruct and LLaDA-1.5. Moreover, for block dLLMs such as LLaDA2.0-mini, it augments the context with a protected terminal [MASK] token to enhance generation quality with negligible overhead.