VOCABTRIM: Vocabulary Pruning for Efficient Speculative Decoding in LLMs

• URL: http://arxiv.org/abs/2506.22694v2
• Date: Thu, 03 Jul 2025 23:08:59 GMT
• Title: VOCABTRIM: Vocabulary Pruning for Efficient Speculative Decoding in LLMs
• Authors: Raghavv Goel, Sudhanshu Agrawal, Mukul Gagrani, Junyoung Park, Yifan Zao, He Zhang, Tian Liu, Yiping Yang, Xin Yuan, Jiuyan Lu, Chris Lott, Mingu Lee,
• Abstract summary: We introduce a training-free technique to improve the performance of drafter-based speculative decoding (SpD) methods.<n>A drafter-based speculative decoding leverages one or more smaller language models, a.k.a. drafters or draft models, to sample a draft sequence or tree consisting of multiple tokens.<n>We show that our method can boost the memory-bound speed-up for Llama-3 models on Spec-Bench, specifically by 16% for Llama-3.2-3B-Instruct.
• Score: 15.508475101753715
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we introduce a simple training-free technique to improve the performance of drafter-based speculative decoding (SpD) methods that incorporates language modeling head (LM head) during drafting process. A drafter-based speculative decoding leverages one or more smaller language models, a.k.a. drafters or draft models, to sample a draft sequence or tree consisting of multiple tokens, followed by verification by a base LLM, a target model, accepting a subset as its valid generation. As it is usually considered that the speculative decoding requires one-to-one mapping between vocabularies of the target model and the draft model, it has been natural to share the vocabulary between them, or even share the LM head as in EAGLE or Medusa. We first identify that this draft token sampling scheme inherently contains an unnecessary inference overhead in drafting, especially for some target LLMs with very large vocabularies. Then, we propose a simple technique, VocabTrim, to mitigate the drafting overhead to improve the generation speed in memory-bound environment. VocabTrim reconstructs the drafter LM head to contain only a limited set of tokens, selected by the most frequently sampled from the vocabulary of the target model. While limiting the vocabulary in drafting slightly degrades the acceptance rate, it significantly reduces the drafting latency in memory-bound process which is often the case on edge devices, resulting in higher memory-bound speed up (MBSU). We show that our method can boost the memory-bound speed-up for Llama-3 models on Spec-Bench, specifically by 16% for Llama-3.2-3B-Instruct.

Related papers

• CARD: Cache-Assisted Parallel Speculative Decoding for Efficient Large Language Model Inference [19.14564724894706]
  We propose a speculative decoding framework employing a 'query-and-correct' paradigm.<n> CARD decouples drafting and verification: the draft model generates candidate tokens to populate a shared cache, while the target model concurrently rectifies the draft model's generation direction.<n>Our approach achieves up to 4.83 speedup over vanilla decoding without requiring fine-tuning of either the draft or target models.
  arXiv  Detail & Related papers  (2025-08-06T14:02:10Z)
• Sampling from Your Language Model One Byte at a Time [82.71473348639489]
  Tokenization can introduce distortion into the model's generations, known as the Prompt Boundary Problem (PBP)<n>We present an inference-time method to convert any autore LM with a BPE tokenizer into a character-level or byte-level LM.<n>Our method efficiently solves the PBP and is also able to unify the vocabularies of language models with different tokenizers.
  arXiv  Detail & Related papers  (2025-06-17T02:37:04Z)
• FR-Spec: Accelerating Large-Vocabulary Language Models via Frequency-Ranked Speculative Sampling [59.8051705468084]
  Speculative sampling has emerged as an important technique for accelerating the auto-regressive generation process of large language models.<n>We present FR-Spec, a frequency-ranked speculative sampling framework that optimize draft candidate selection through vocabulary space compression.
  arXiv  Detail & Related papers  (2025-02-20T18:58:10Z)
• 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)
• Graph-Structured Speculative Decoding [52.94367724136063]
  Speculative decoding has emerged as a promising technique to accelerate the inference of Large Language Models. We introduce an innovative approach utilizing a directed acyclic graph (DAG) to manage the drafted hypotheses. We observe a remarkable speedup of 1.73$times$ to 1.96$times$, significantly surpassing standard speculative decoding.
  arXiv  Detail & Related papers  (2024-07-23T06:21:24Z)
• Direct Alignment of Draft Model for Speculative Decoding with Chat-Fine-Tuned LLMs [11.245862832561176]
  Training a high-quality draft model is required to enable inference acceleration via speculative decoding. We train Llama 2 Chat Drafter 115M, a draft model for Llama 2 Chat 7B or larger, with only 1.64% of the original size. Our results show that Llama 2 Chat Drafter 115M with speculative decoding achieves up to 2.3 block efficiency and 2.4$times$ speed-up.
  arXiv  Detail & Related papers  (2024-02-29T19:55:06Z)
• 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)
• Decoding Speculative Decoding [4.56754610152086]
  Speculative Decoding is a technique to speed up inference for Large Language Models without sacrificing quality.<n>We study over 350 experiments with LLaMA-65B and OPT-66B using speculative decoding.<n>Our newly designed draft model can provide 111% higher throughput than existing draft models.
  arXiv  Detail & Related papers  (2024-02-02T16:15:24Z)
• 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)
• The Ups and Downs of Large Language Model Inference with Vocabulary Trimming by Language Heuristics [74.99898531299148]
  This research examines vocabulary trimming (VT) inspired by restricting embedding entries to the language of interest to bolster time and memory efficiency. We apply two languages to trim the full vocabulary - Unicode-based script filtering and corpus-based selection - to different language families and sizes. It is found that VT reduces the memory usage of small models by nearly 50% and has an upper bound of 25% improvement in generation speed.
  arXiv  Detail & Related papers  (2023-11-16T09:35:50Z)

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.