arXiv Machine Learning
Sep 16

Window-Diffusion: Accelerating Diffusion Language Model Inference with Windowed Token Pruning and Caching

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.

By Fengrui Zuo, Zhiwei Ke, Yiming Liu, Wenqi Lou, Chao Wang, Xuehai Zhou
arXiv Machine Learning
Sep 14

Fixed State, Long Reach: What a Constant-Size Cache Buys Block Diffusion at Scale

The paper investigates how block‑diffusion language models can use a constant‑size cache to enable efficient parallel decoding. By employing sequence mixers that summarize completed blocks into a reusable state and a block‑causal training objective, the authors pretrain three 3B block‑diffusion denoisers (attention, Mamba, and hybrid) on 300 B tokens. The resulting state‑space cache remains O(1) in memory and latency regardless of context length, yielding significant speed‑up and memory savings compared to traditional attention‑based caches, especially at very long sequences.

By Vaibhav Singh, Pierre-Andr\'e No\"el, Torsten Scholak, Eugene Belilovsky, Oleksiy Ostapenko
arXiv Machine Learning
Aug 31

Learned Relay Representations for Forward-Thinking Discrete Diffusion Models

The paper introduces Learned Relay Representations (Relay), a technique for Masked Diffusion Models (MDMs) that preserves and forwards internal latent information across denoising steps via a differentiable per-token channel trained with truncated backpropagation through time. Relay enables MDMs to be forward‑thinking, avoiding costly recomputation of internal representations and improving efficiency. Applied to Fast‑dLLM v2, Relay outperforms standard supervised fine‑tuning on coding tasks and reduces inference latency by up to 32%, demonstrating a clear performance‑latency advantage for diffusion language models.

By Benjamin Rozonoyer, Jacopo Minniti, Dhruvesh Patel, Neil Band, Avishek Joey Bose, Tim G. J. Rudner, Andrew McCallum