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arXiv:2605. 29233v2 Announce Type: replace-cross Abstract: Diffusion language models (dLLMs) generate text by iteratively denoising multiple token positions in parallel, offering an attractive alternative to strictly autoregressive decoding.
The paper introduces Window-Diffusion, a method that accelerates diffusion language model inference by pruning and caching tokens within a sliding window. It categorizes undecoded tokens into active, buffer, and far-field groups, computing only the first two while discarding the rest. Experiments on LLaDA and Dream demonstrate up to 99× speedup with minimal loss in generation quality.
DynaTE is a hardware–software co‑design that accelerates diffusion‑based large language models (dLLMs) by dynamically adapting execution to token states. It skips low‑utility token computations, uses a dimension‑reconfigurable processing array, refines locally dependent tokens with FLDD, and streams vocabulary to handle irregular token patterns. These techniques yield 2.05–2.78× speedup and 2.99–3.93× higher energy efficiency compared to existing dLLM accelerators, and outperform Jetson AGX Orin by 2.55× speedup and 6.07× energy efficiency.
The paper proposes Predictive-State Flows (PSF), a new architecture for continuous diffusion language models that separates the diffusion prior’s role to generate high‑level predictive states and the decoder’s role to resolve token‑level choices. PSF partitions sequences into blocks, uses a rectified‑flow prior to produce predictive states, and then a block‑causal decoder generates all blocks in parallel, reducing token‑level sequential depth. Experiments on OpenWebText show that a 197M‑parameter PSF model with eight predictive states achieves a generative perplexity of 22.8 and a MAUVE@512 of 0.93 while using far fewer FLOPs than a comparable ELF‑B model.
Diffusion Language Models (DLMs) exhibit strong parallel decoding capabilities by denoising multiple tokens in a single generation step. However, this parallelism comes with substantial computational...
The paper introduces a training‑free structured suffix modeling technique to accelerate Diffusion Language Models (DLMs). It partitions the suffix into local, middle, and tail regions, retaining varying numbers of tokens per region and incorporating previous decoding results into current token representations. Experiments on three DLMs show significant speedups—up to 72.81× in long‑sequence inference—while often improving performance, and the method is compatible with existing acceleration strategies.