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Computer Science > Computation and Language

arXiv:2511.08923 (cs)
[Submitted on 12 Nov 2025]

Title:TiDAR: Think in Diffusion, Talk in Autoregression

Authors:Jingyu Liu, Xin Dong, Zhifan Ye, Rishabh Mehta, Yonggan Fu,
Vartika Singh, Jan Kautz, Ce Zhang, Pavlo Molchanov
View a PDF of the paper titled TiDAR: Think in Diffusion, Talk in
Autoregression, by Jingyu Liu and 8 other authors
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    Abstract:Diffusion language models hold the promise of fast
    parallel generation, while autoregressive (AR) models typically
    excel in quality due to their causal structure aligning naturally
    with language modeling. This raises a fundamental question: can
    we achieve a synergy with high throughput, higher GPU
    utilization, and AR level quality? Existing methods fail to
    effectively balance these two aspects, either prioritizing AR
    using a weaker model for sequential drafting (speculative
    decoding), leading to lower drafting efficiency, or using some
    form of left-to-right (AR-like) decoding logic for diffusion,
    which still suffers from quality degradation and forfeits its
    potential parallelizability. We introduce TiDAR, a sequence-level
    hybrid architecture that drafts tokens (Thinking) in Diffusion
    and samples final outputs (Talking) AutoRegressively - all within
    a single forward pass using specially designed structured
    attention masks. This design exploits the free GPU compute
    density, achieving a strong balance between drafting and
    verification capacity. Moreover, TiDAR is designed to be
    serving-friendly (low overhead) as a standalone model. We
    extensively evaluate TiDAR against AR models, speculative
    decoding, and diffusion variants across generative and likelihood
    tasks at 1.5B and 8B scales. Thanks to the parallel drafting and
    sampling as well as exact KV cache support, TiDAR outperforms
    speculative decoding in measured throughput and surpasses
    diffusion models like Dream and Llada in both efficiency and
    quality. Most notably, TiDAR is the first architecture to close
    the quality gap with AR models while delivering 4.71x to 5.91x
    more tokens per second.

Comments: NVIDIA-Tech Report
Subjects: Computation and Language (cs.CL); Artificial Intelligence
          (cs.AI)
Cite as:  arXiv:2511.08923 [cs.CL]
          (or arXiv:2511.08923v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2511.08923
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Jingyu Liu [view email]
[v1] Wed, 12 Nov 2025 02:59:33 UTC (652 KB)
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