SpecTr-GBV: Multi-Draft Block Verification Accelerating Speculative Decoding

• URL: http://arxiv.org/abs/2604.25925v1
• Date: Wed, 01 Apr 2026 11:52:46 GMT
• Title: SpecTr-GBV: Multi-Draft Block Verification Accelerating Speculative Decoding
• Abstract summary: We propose SpecTr-GBV, a novel SD method that unifies multi-draft and greedy block verification into a single framework.<n>We theoretically prove that SpecTr-GBV achieves the optimal expected acceptance length physically attainable within the framework of i.i.d. draft generation.<n>Our method achieves superior speedup and significantly higher block efficiency while preserving output quality.
• Score: 5.247507695462136
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autoregressive language models suffer from high inference latency due to their sequential decoding nature. Speculative decoding (SD) mitigates this by employing a lightweight draft model to propose candidate tokens, which are selectively verified by a larger target model. While existing methods either adopt multi-draft strategies to increase acceptance rates or block verification techniques to jointly verify multiple tokens, they remain limited by treating these improvements in isolation. In this work, we propose SpecTr-GBV, a novel SD method that unifies multi-draft and greedy block verification (GBV) into a single framework. By formulating the verification step as an optimal transport problem over draft and target token blocks, SpecTr-GBV improves both theoretical efficiency and empirical performance. We theoretically prove that SpecTr-GBV achieves the optimal expected acceptance length physically attainable within the framework of i.i.d. draft generation, and this bound improves as the number of drafts increases. Empirically, we evaluate SpecTr-GBV across five datasets and four baselines. Our method achieves superior speedup and significantly higher block efficiency while preserving output quality. In addition, we perform comprehensive ablation studies to evaluate the impact of various hyperparameters in the model.

Related papers

• BlockPilot: Instance-Adaptive Policy Learning for Diffusion-based Speculative Decoding [20.29443489871258]
  Speculative decoding improves parallelism by generating multiple tokens per forward pass via block-level diffusion.<n>Existing methods adopt a fixed inference block size and assume a uniform optimal decoding strategy across all inputs.<n>We show that this assumption is suboptimal, as the optimal block size varies across samples and plays a critical role in speculative decoding performance.<n>We propose BlockPilot, a sample-adaptive policy that predicts the optimal block size from the prefilling representation.
  arXiv  Detail & Related papers  (2026-06-30T08:24:05Z)
• DIVERSED: Relaxed Speculative Decoding via Dynamic Ensemble Verification [29.426184837710952]
  Speculative decoding is an effective technique for accelerating large language model inference by drafting multiple tokens in parallel.<n>We propose Dynamic Verification Relaxed Speculative Decoding (DIVERSED), a relaxed verification framework that improves time efficiency while preserving generation quality.<n>We demonstrate that DIVERSED achieves substantially higher inference efficiency compared to standard speculative decoding methods.
  arXiv  Detail & Related papers  (2026-04-08T21:52:32Z)
• TABED: Test-Time Adaptive Ensemble Drafting for Robust Speculative Decoding in LVLMs [14.030784220154151]
  We propose Test-time Adaptive Batched Ensemble Drafting (TABED) for Large Vision-Language Models.<n> TABED ensembles multiple drafts obtained via batch inference by leveraging deviations from past ground truths available in the SD setting.<n>It achieves an average robust walltime speedup of 1.74x over autoregressive decoding and a 5% improvement over single drafting methods.
  arXiv  Detail & Related papers  (2026-01-28T08:16:57Z)
• Training-Free Loosely Speculative Decoding: Accepting Semantically Correct Drafts Beyond Exact Match [21.810129153556044]
  Training-Free Loosely Speculative Decoding (FLy) is a novel method that loosens the rigid verification criterion.<n>We show that FLy preserves more than 99% of the target model's accuracy while achieving an average 2.81x speedup.
  arXiv  Detail & Related papers  (2025-11-28T08:23:30Z)
• 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)
• Towards Optimal Multi-draft Speculative Decoding [102.67837141152232]
  Multi-Draft Speculative Decoding (MDSD) is a recent approach where, when generating each token, a small draft model generates multiple drafts.<n>This paper discusses the dual of the optimal transport problem, providing a way to efficiently compute the optimal acceptance rate.
  arXiv  Detail & Related papers  (2025-02-26T03:22:44Z)
• TETRIS: Optimal Draft Token Selection for Batch Speculative Decoding [76.23719557942917]
  TETRIS actively selects the most promising draft tokens (for every request in a batch) to be accepted when verified in parallel.<n>We show theoretically and empirically that TETRIS outperforms baseline speculative decoding and existing methods that dynamically select draft tokens.
  arXiv  Detail & Related papers  (2025-02-21T04:19:24Z)
• Improving Multi-candidate Speculative Decoding [1.6291177798903276]
  Speculative Decoding (SD) is a technique to accelerate the inference of Large Language Models (LLMs)<n>In this work, we introduce a new version of MCSD that includes a target model multi-candidate generation.<n>We also evaluate the effects of using the target model multi-candidate process with different draft models on output quality.
  arXiv  Detail & Related papers  (2024-09-16T18:20:38Z)
• 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)
• 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.