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Fast-dLLM v2: Efficient Block-Diffusion LLM research paper by NVIDIA, 2025

NVIDIA · Sep 30, 2025 · Inference and efficiency · 125 citations · 59 upvotes · unverified

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What it shows

Fast-dLLM v2, a block diffusion language model, efficiently converts pretrained autoregressive models for parallel text generation, achieving significant speedup without compromising accuracy.

UnverifiedHugging Face's summary; not yet checked by hand.

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About this paper
Authors
Chengyue Wu, Hao Zhang, Shuchen Xue and 7 more
arXiv
2509.26328 · PDF
Venue
arXiv.org
Citations
125, 18 influential · Semantic Scholar
Upvotes
59 · Hugging Face
Code
github.com/NVlabs/Fast-dLLM
Lab
NVIDIA · on Companies · on Acquisitions · on Quarterly · on Paydays · on TechConf

Changes

What changed
Influential citationsfirst count: 18Sep 25, 2026
Citationsfirst count: 125Sep 25, 2026
New paperFound by the weekly scan, unverifiedSep 25, 2026

Sources: each lab's own papers and arXiv, with citation and upvote counts from Semantic Scholar and Hugging Face. One-line summaries are for orientation, not a substitute for the paper. Logos via logo.dev; trademarks belong to their owners.

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