arXiv:2509. 18085v4 Announce Type: replace-cross Abstract: Diffusion LLMs (dLLMs) have recently emerged as a powerful alternative to autoregressive LLMs (AR-LLMs) with the potential to operate at significantly higher token-generation rates.
By Sudhanshu Agrawal, Risheek Garrepalli, Raghavv Goel, Christopher Lott, Fatih Porikli, Mingu Lee
arXiv:2606. 04446v1 Announce Type: cross Abstract: Speculative decoding accelerates autoregressive large language model inference by drafting multiple tokens and verifying them in a single target-model forward pass.
By Liyuan Zhang, Jiarui Zhang, Jinwei Yao, Ran Yan, Yuchen Yang, Jiahao Zhang, Tongkai Yang, Yi Wu, Binhang Yuan
arXiv:2607. 22634v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to autoregressive (AR) LLMs, generating tokens in parallel.
By Zheng Wang, Zhifan Ye, Qi Cheng, Yonggan Fu, Ziyan Wang, Feng Zhu, Haozhe Zhao, Jan Kautz, Pavlo Molchanov, Humphrey Shi, Minjia Zhang
arXiv:2608. 08721v1 Announce Type: cross Abstract: Speculative decoding accelerates large language model inference by drafting multiple tokens for parallel verification, with efficiency critically determined by the speculative length selected at each decoding round.
By Zexun Lin, Yuan Feng, Junlin Lv, Kevin S. Zhou, Xike Xie
arXiv:2606. 00487v1 Announce Type: new Abstract: Using a diffusion model for parallel drafting is a promising approach for speculative decoding.
By Zhuoyu Wang, Junnan Huang, Xinyu Chen
Speculative decoding accelerates sampling from an autoregressive LLM by using a faster auxiliary model to draft tokens which are then verified in parallel by the LLM. Standard speculative decoding is lossless: its rejection and resampling steps exactly preserve the LLM's sampling distribution.