DiffuSpec: Unlocking Diffusion Language Models for Speculative Decoding

• URL: http://arxiv.org/abs/2510.02358v1
• Date: Sun, 28 Sep 2025 07:00:15 GMT
• Title: DiffuSpec: Unlocking Diffusion Language Models for Speculative Decoding
• Authors: Guanghao Li, Zhihui Fu, Min Fang, Qibin Zhao, Ming Tang, Chun Yuan, Jun Wang,
• Abstract summary: We present DiffuSpec, a training-free drop-in framework that uses a pretrained diffusion language model (DLM) to produce multi-token drafts in a single forward pass.<n>Across benchmarks, DiffuSpec yields up to 3x wall-clock speedup, establishing diffusion-based drafting as a robust alternative to autoregressive drafters for speculative decoding.
• Score: 66.40658898418316
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As large language models (LLMs) scale up, accuracy improves, but the autoregressive (AR) nature of decoding increases latency since each token requires a serial forward pass. Speculative decoding addresses this by employing a fast drafter to propose multi-token drafts, which are then verified in parallel by the target model. However, many deployments still rely on AR drafters, where sequential passes limit wall-clock gains. We revisit the drafting stage and present DiffuSpec, a training-free drop-in framework that uses a pretrained diffusion language model (DLM) to produce multi-token drafts in a single forward pass, while remaining compatible with standard AR verifiers. Because DLM drafts are generated under bidirectional conditioning, parallel per-position candidates form a token lattice in which the locally highest-probability token at each position need not form a causal left-to-right path. Moreover, DLM drafting requires pre-specifying a draft length, inducing a speed-quality trade-off. To address these challenges, we introduce two practical components: (i) a causal-consistency path search (CPS) over this lattice that extracts a left-to-right path aligned with AR verification; and (ii) an adaptive draft-length (ADL) controller that adjusts next proposal size based on recent acceptance feedback and realized generated length. Across benchmarks, DiffuSpec yields up to 3x wall-clock speedup, establishing diffusion-based drafting as a robust alternative to autoregressive drafters for speculative decoding.

Related papers

• Training-Free Acceleration for Document Parsing Vision-Language Model with Hierarchical Speculative Decoding [102.88996030431662]
  We propose a training-free and highly efficient acceleration method for document parsing tasks.<n>Inspired by speculative decoding, we employ a lightweight document parsing pipeline as a draft model to predict batches of future tokens.<n>We demonstrate the effectiveness of our approach on the general-purpose OmniDocBench.
  arXiv  Detail & Related papers  (2026-02-13T14:22:10Z)
• PACER: Blockwise Pre-verification for Speculative Decoding with Adaptive Length [21.738896310075678]
  Speculative decoding (SD) is a powerful technique for accelerating the inference process of large language models (LLMs)<n>We propose Pacer, a novel approach that dynamically controls draft length using a lightweight, trainable pre-verification layer.<n>Our results demonstrate that Pacer achieves up to 2.66x Speedup over autoregressive decoding and consistently outperforms standard speculative decoding.
  arXiv  Detail & Related papers  (2026-02-01T15:12:38Z)
• DEER: Draft with Diffusion, Verify with Autoregressive Models [33.19684425811274]
  Speculative decoding mitigates the inherent latency of autoregressive decoding.<n>We introduce DEER, an efficient speculative decoding framework.<n>Experiments show DEER reaches draft acceptance lengths of up to 32 tokens.
  arXiv  Detail & Related papers  (2025-12-17T08:19:04Z)
• SpecDiff-2: Scaling Diffusion Drafter Alignment For Faster Speculative Decoding [48.96349422252313]
  Speculative decoding has become the standard approach for accelerating Large Language Model (LLM) inference.<n>It exploits a lossless draft-then-verify procedure to circumvent the latency of autoregressive decoding, achieving impressive speed-ups.<n>This paper proposes SpecDiff-2, a novel framework to jointly address these two bottlenecks.
  arXiv  Detail & Related papers  (2025-11-01T16:12:56Z)
• Fast Inference via Hierarchical Speculative Decoding [65.40448210801763]
  We introduce Hierarchical Speculative Decoding (HSD), an algorithm that stacks draft models into a hierarchy, where each model proposes tokens, and the next larger model verifies them in a single forward pass.<n>HSD gives up to 1.2x speed-up over the best single-draft baseline.
  arXiv  Detail & Related papers  (2025-10-22T15:56:19Z)
• CARD: A Cache-Assisted Parallel Speculative Decoding Framework via Query-and-Correct Paradigm for Accelerating LLM Inference [14.527697328189362]
  We propose a speculative decoding framework called CARD, which employs a novel query-and-correct paradigm.<n>Our approach decouples drafting from verification, effectively leveraging the draft model's efficiency without additional fine-tuning.<n> CARD significantly outperforms existing state-of-the-art methods, achieving up to a 4.83x acceleration over vanilla autoregressive decoding.
  arXiv  Detail & Related papers  (2025-08-06T14:02:10Z)
• Think Before You Accept: Semantic Reflective Verification for Faster Speculative Decoding [48.52389201779425]
  Speculative decoding accelerates inference by generating multiple draft tokens using a lightweight model and verifying them in parallel.<n>Existing verification methods rely heavily on distributional consistency while overlooking semantic correctness.<n>We propose Reflective Verification, a training-free and semantics-aware approach that achieves a better trade-off between correctness and efficiency.
  arXiv  Detail & Related papers  (2025-05-24T10:26:27Z)
• PEARL: Parallel Speculative Decoding with Adaptive Draft Length [12.166703341906242]
  We propose a conceptually simple, flexible, and general framework to boost speculative decoding, namely Parallel spEculative decoding with Adaptive dRaft Length (PEARL)<n>PEARL proposes pre-verify to verify the first draft token in advance during the drafting phase, and post-verify to generate more draft tokens during the verification phase.<n> Experiments on various text generation benchmarks demonstrate the effectiveness of our PEARL, leading to a superior speed up performance up to 4.43$times$ and 1.50$times$, compared to auto-regressive decoding and vanilla speculative decoding, respectively.
  arXiv  Detail & Related papers  (2024-08-13T08:32:06Z)
• Speculative Diffusion Decoding: Accelerating Language Generation through Diffusion [55.0194604505437]
  Speculative decoding has emerged as a widely adopted method to accelerate large language model inference.<n>This paper proposes an adaptation of speculative decoding which uses discrete diffusion models to generate draft sequences.
  arXiv  Detail & Related papers  (2024-08-10T21:24:25Z)
• Ouroboros: Generating Longer Drafts Phrase by Phrase for Faster Speculative Decoding [65.94521678103237]
  Speculative decoding is a widely used method that accelerates the generation process of large language models. We introduce Ouroboros, which can generate draft phrases to parallelize the drafting process. Ouroboros can achieve speedups of up to $2.8times$ over speculative decoding and $3.9times$ over vanilla decoding.
  arXiv  Detail & Related papers  (2024-02-21T11:31:28Z)
• Multi-Candidate Speculative Decoding [82.05519287513444]
  Large language models have shown impressive capabilities across a variety of NLP tasks, yet their generating text autoregressively is time-consuming. One way to speed them up is speculative decoding, which generates candidate segments from a fast draft model that is then verified in parallel by the target model. This paper proposes sampling multiple candidates from a draft model and then organising them in batches for verification. We design algorithms for efficient multi-candidate verification while maintaining the distribution of the target model.
  arXiv  Detail & Related papers  (2024-01-12T17:15:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.