Retrofitting Linear Attention into Diffusion Language Models
Diffusion language models (dLLMs) offer a promising alternative to autoregressive models by accelerating inference through parallel decoding. Recent dLLMs commonly use blockwise semi-autoregressive decoding, generating blocks autoregressively while denoising tokens within each active block in parallel. However, despite KV caching, each denoising step still attends to all previous blocks, repeatedly incurring prefix-attention cost. Motivated by this bottleneck, we ask whether dLLM inference can be further accelerated by linearizing attention over previous blocks. We introduce block-hybrid attention, which retains exact softmax attention within the active denoising block while applying line...
| Authors | Jinha Kim, Younghun Roh, Jaeyeon Kim |
|---|---|
| Primary category | cs.LG |
| Published | 6 August 2026 |
| Last updated at source | 6 August 2026 |
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