@inproceedings{fu-etal-2026-nemotron,
title = "Nemotron-Labs-Diffusion: A Tri-Mode Language Model Unifying Autoregressive, Diffusion, and Self-Speculation Decoding",
author = "Fu, Yonggan and
Whalen, Lexington and
Garg, Abhinav and
Wu, Chengyue and
Khadkevich, Maksim and
Oswald, Nicolai and
Xie, Enze and
Egert, Daniel and
Turuvekere Sreenivas, Sharath and
Diao, Shizhe and
Yu, Chenhan D. and
Yu, Ye and
Chen, Weijia and
Norouzi, Sajad and
Liu, Jingyu and
Lan, Shiyi and
Zhu, Ligeng and
Wang, Jin and
Jiang, Jindong and
Mardani, Morteza and
Maghoumi, Mehran and
Han, Song and
Juki{\'c}, Ante and
Tajbakhsh, Nima and
Kautz, Jan and
Molchanov, Pavlo",
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.2416/",
pages = "54868--54887",
ISBN = "979-8-89176-459-0",
abstract = "Diffusion language models (LMs) enable parallel decoding, but still lag behind autoregressive (AR) LMs in accuracy, and behind multi-token prediction (MTP) methods in efficiency. This raises three questions: should diffusion LMs compete with AR LMs or be harmonized with them; can diffusion provide a stronger acceleration mechanism than MTP; and does diffusion decoding have sufficient long-term potential to justify further exploration? We study these questions through Nemotron-Labs-Diffusion, a tri-mode LM that unifies three decoding modes{---}AR, diffusion, and self-speculation (diffusion drafts, AR verifies){---}within a single model to sustain high throughput across all concurrency levels. Our study shows that (1) AR and diffusion objectives are mutually beneficial rather than competing, (2) diffusion-based self-speculation achieves higher acceptance lengths and better system vs. per-user throughput trade-offs than MTP baselines, and (3) diffusion decoding shows strong potential for parallel decoding under an optimal sampler in a speed-of-light analysis. Based on these insights, we develop the Nemotron-Labs-Diffusion family at 3B, 8B, and 14B scales. Our models outperform state-of-the-art open-source AR and diffusion LMs in both accuracy and speed, e.g., achieving 6x tokens per forward over Qwen3-8B with comparable benchmark accuracy, translating to 4x throughput on SPEED-Bench measured with SGLang on an NVIDIA GB200 GPU."
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<abstract>Diffusion language models (LMs) enable parallel decoding, but still lag behind autoregressive (AR) LMs in accuracy, and behind multi-token prediction (MTP) methods in efficiency. This raises three questions: should diffusion LMs compete with AR LMs or be harmonized with them; can diffusion provide a stronger acceleration mechanism than MTP; and does diffusion decoding have sufficient long-term potential to justify further exploration? We study these questions through Nemotron-Labs-Diffusion, a tri-mode LM that unifies three decoding modes—AR, diffusion, and self-speculation (diffusion drafts, AR verifies)—within a single model to sustain high throughput across all concurrency levels. Our study shows that (1) AR and diffusion objectives are mutually beneficial rather than competing, (2) diffusion-based self-speculation achieves higher acceptance lengths and better system vs. per-user throughput trade-offs than MTP baselines, and (3) diffusion decoding shows strong potential for parallel decoding under an optimal sampler in a speed-of-light analysis. Based on these insights, we develop the Nemotron-Labs-Diffusion family at 3B, 8B, and 14B scales. Our models outperform state-of-the-art open-source AR and diffusion LMs in both accuracy and speed, e.g., achieving 6x tokens per forward over Qwen3-8B with comparable benchmark accuracy, translating to 4x throughput on SPEED-Bench measured with SGLang on an NVIDIA GB200 GPU.</abstract>
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%0 Conference Proceedings
%T Nemotron-Labs-Diffusion: A Tri-Mode Language Model Unifying Autoregressive, Diffusion, and Self-Speculation Decoding
%A Fu, Yonggan
%A Whalen, Lexington
%A Garg, Abhinav
%A Wu, Chengyue
%A Khadkevich, Maksim
%A Oswald, Nicolai
%A Xie, Enze
%A Egert, Daniel
%A Turuvekere Sreenivas, Sharath
%A Diao, Shizhe
%A Yu, Chenhan D.
%A Yu, Ye
%A Chen, Weijia
%A Norouzi, Sajad
%A Liu, Jingyu
%A Lan, Shiyi
%A Zhu, Ligeng
%A Wang, Jin
%A Jiang, Jindong
%A Mardani, Morteza
%A Maghoumi, Mehran
%A Han, Song
%A Jukić, Ante
%A Tajbakhsh, Nima
%A Kautz, Jan
%A Molchanov, Pavlo
%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 fu-etal-2026-nemotron
%X Diffusion language models (LMs) enable parallel decoding, but still lag behind autoregressive (AR) LMs in accuracy, and behind multi-token prediction (MTP) methods in efficiency. This raises three questions: should diffusion LMs compete with AR LMs or be harmonized with them; can diffusion provide a stronger acceleration mechanism than MTP; and does diffusion decoding have sufficient long-term potential to justify further exploration? We study these questions through Nemotron-Labs-Diffusion, a tri-mode LM that unifies three decoding modes—AR, diffusion, and self-speculation (diffusion drafts, AR verifies)—within a single model to sustain high throughput across all concurrency levels. Our study shows that (1) AR and diffusion objectives are mutually beneficial rather than competing, (2) diffusion-based self-speculation achieves higher acceptance lengths and better system vs. per-user throughput trade-offs than MTP baselines, and (3) diffusion decoding shows strong potential for parallel decoding under an optimal sampler in a speed-of-light analysis. Based on these insights, we develop the Nemotron-Labs-Diffusion family at 3B, 8B, and 14B scales. Our models outperform state-of-the-art open-source AR and diffusion LMs in both accuracy and speed, e.g., achieving 6x tokens per forward over Qwen3-8B with comparable benchmark accuracy, translating to 4x throughput on SPEED-Bench measured with SGLang on an NVIDIA GB200 GPU.
%U https://aclanthology.org/2026.emnlp-main.2416/
%P 54868-54887
Markdown (Informal)
[Nemotron-Labs-Diffusion: A Tri-Mode Language Model Unifying Autoregressive, Diffusion, and Self-Speculation Decoding](https://aclanthology.org/2026.emnlp-main.2416/) (Fu et al., EMNLP 2026)
ACL
- Yonggan Fu, Lexington Whalen, Abhinav Garg, Chengyue Wu, Maksim Khadkevich, Nicolai Oswald, Enze Xie, Daniel Egert, Sharath Turuvekere Sreenivas, Shizhe Diao, Chenhan D. Yu, Ye Yu, Weijia Chen, Sajad Norouzi, Jingyu Liu, Shiyi Lan, Ligeng Zhu, Jin Wang, Jindong Jiang, Morteza Mardani, Mehran Maghoumi, Song Han, Ante Jukić, Nima Tajbakhsh, Jan Kautz, and Pavlo Molchanov. 2026. Nemotron-Labs-Diffusion: A Tri-Mode Language Model Unifying Autoregressive, Diffusion, and Self-Speculation Decoding. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 54868–54887, Budapest, Hungary. Association for Computational Linguistics.