@inproceedings{liu-etal-2026-sequential,
title = "Sequential Diffusion Language Models",
author = "Liu, Yangzhou and
Cao, Yue and
Li, Hao and
Luo, Gen and
Wang, Shihao and
Chen, Zhe and
Wang, Weiyun and
Liang, Xiaobo and
Qi, Biqing and
Wu, Lijun and
Tian, Changyao and
Zhang, Yanting and
Li, Yuqiang and
Lu, Tong and
Qiao, Yu and
Dai, Jifeng and
Wang, Wenhai",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2288/",
pages = "52058--52076",
ISBN = "979-8-89176-459-0",
abstract = "Diffusion language models (DLMs) are theoretically efficient but are limited by fixed-length decoding and poor compatibility with KV caches. Block diffusion mitigates these issues, yet still enforces a fixed block size and costly training. We introduce Next Sequence Prediction (NSP), which unifies next token and next block prediction, enabling the model to adaptively determine generation length at each step. NSP reduces to standard next token prediction when the length is one. Building on NSP, we propose the Sequential Diffusion Language Model (SDLM), which retrofits pretrained autoregressive language models (ALMs) at minimal cost. Specifically, SDLM performs diffusion inference within fixed-size mask blocks, but dynamically decodes contiguous subsequences based on model confidence, thereby preserving KV-cache compatibility and improving robustness to the varying uncertainty and semantics across the sequence. SDLM matches or surpasses strong autoregressive baselines with only 3.5M training samples, while achieving 2.0{\texttimes}{~}3.4{\texttimes} higher throughput than Qwen2.5 across three model scales (3B, 7B, 32B) with confidence-based decoding. Furthermore, when sampling with self-speculative decoding, SDLM attains up to 5.7{\texttimes} end-to-end speedup measured as wall-clock tokens per second (TPS). Notably, SDLM-32B achieves performance comparable to Qwen2.5-32B with up to 2.5{\texttimes} speedup, demonstrating the scalability of our approach. Project page and codes: https://github.com/OpenGVLab/SDLM"
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<abstract>Diffusion language models (DLMs) are theoretically efficient but are limited by fixed-length decoding and poor compatibility with KV caches. Block diffusion mitigates these issues, yet still enforces a fixed block size and costly training. We introduce Next Sequence Prediction (NSP), which unifies next token and next block prediction, enabling the model to adaptively determine generation length at each step. NSP reduces to standard next token prediction when the length is one. Building on NSP, we propose the Sequential Diffusion Language Model (SDLM), which retrofits pretrained autoregressive language models (ALMs) at minimal cost. Specifically, SDLM performs diffusion inference within fixed-size mask blocks, but dynamically decodes contiguous subsequences based on model confidence, thereby preserving KV-cache compatibility and improving robustness to the varying uncertainty and semantics across the sequence. SDLM matches or surpasses strong autoregressive baselines with only 3.5M training samples, while achieving 2.0× 3.4× higher throughput than Qwen2.5 across three model scales (3B, 7B, 32B) with confidence-based decoding. Furthermore, when sampling with self-speculative decoding, SDLM attains up to 5.7× end-to-end speedup measured as wall-clock tokens per second (TPS). Notably, SDLM-32B achieves performance comparable to Qwen2.5-32B with up to 2.5× speedup, demonstrating the scalability of our approach. Project page and codes: https://github.com/OpenGVLab/SDLM</abstract>
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%0 Conference Proceedings
%T Sequential Diffusion Language Models
%A Liu, Yangzhou
%A Cao, Yue
%A Li, Hao
%A Luo, Gen
%A Wang, Shihao
%A Chen, Zhe
%A Wang, Weiyun
%A Liang, Xiaobo
%A Qi, Biqing
%A Wu, Lijun
%A Tian, Changyao
%A Zhang, Yanting
%A Li, Yuqiang
%A Lu, Tong
%A Qiao, Yu
%A Dai, Jifeng
%A Wang, Wenhai
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F liu-etal-2026-sequential
%X Diffusion language models (DLMs) are theoretically efficient but are limited by fixed-length decoding and poor compatibility with KV caches. Block diffusion mitigates these issues, yet still enforces a fixed block size and costly training. We introduce Next Sequence Prediction (NSP), which unifies next token and next block prediction, enabling the model to adaptively determine generation length at each step. NSP reduces to standard next token prediction when the length is one. Building on NSP, we propose the Sequential Diffusion Language Model (SDLM), which retrofits pretrained autoregressive language models (ALMs) at minimal cost. Specifically, SDLM performs diffusion inference within fixed-size mask blocks, but dynamically decodes contiguous subsequences based on model confidence, thereby preserving KV-cache compatibility and improving robustness to the varying uncertainty and semantics across the sequence. SDLM matches or surpasses strong autoregressive baselines with only 3.5M training samples, while achieving 2.0× 3.4× higher throughput than Qwen2.5 across three model scales (3B, 7B, 32B) with confidence-based decoding. Furthermore, when sampling with self-speculative decoding, SDLM attains up to 5.7× end-to-end speedup measured as wall-clock tokens per second (TPS). Notably, SDLM-32B achieves performance comparable to Qwen2.5-32B with up to 2.5× speedup, demonstrating the scalability of our approach. Project page and codes: https://github.com/OpenGVLab/SDLM
%U https://aclanthology.org/2026.emnlp-main.2288/
%P 52058-52076
Markdown (Informal)
[Sequential Diffusion Language Models](https://aclanthology.org/2026.emnlp-main.2288/) (Liu et al., EMNLP 2026)
ACL
- Yangzhou Liu, Yue Cao, Hao Li, Gen Luo, Shihao Wang, Zhe Chen, Weiyun Wang, Xiaobo Liang, Biqing Qi, Lijun Wu, Changyao Tian, Yanting Zhang, Yuqiang Li, Tong Lu, Yu Qiao, Jifeng Dai, and Wenhai Wang. 2026. Sequential Diffusion Language Models. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 52058–52076, Budapest, Hungary. Association for Computational Linguistics.