@inproceedings{zhao-etal-2025-fr,
title = "{FR}-Spec: Accelerating Large-Vocabulary Language Models via Frequency-Ranked Speculative Sampling",
author = "Zhao, Weilin and
Pan, Tengyu and
Han, Xu and
Zhang, Yudi and
Sun, Ao and
Huang, Yuxiang and
Zhang, Kaihuo and
Zhao, Weilun and
Li, Yuxuan and
Zhou, Jie and
Zhou, Hao and
Wang, Jianyong and
Liu, Zhiyuan and
Sun, Maosong",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.198/",
doi = "10.18653/v1/2025.acl-long.198",
pages = "3909--3921",
ISBN = "979-8-89176-251-0",
abstract = "Speculative sampling has emerged as an important technique for accelerating the auto-regressive generation process of large language models (LLMs) by utilizing a draft-then-verify mechanism to produce multiple tokens per forward pass. While state-of-the-art speculative sampling methods use only a single layer and a language modeling (LM) head as the draft model to achieve impressive layer compression, their efficiency gains are substantially reduced for large-vocabulary LLMs, such as Llama-3-8B with a vocabulary of 128k tokens. To address this, we present FR-Spec, a frequency-ranked speculative sampling framework that optimizes draft candidate selection through vocabulary space compression. By constraining the draft search to a frequency-prioritized token subset, our method reduces LM Head computation overhead by 75{\%} while ensuring the equivalence of the final output distribution. Experiments across multiple datasets demonstrate an average of 1.12$\times$ speedup over the state-of-the-art speculative sampling method EAGLE-2. Code is availableat https://github.com/thunlp/FR-Spec."
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<abstract>Speculative sampling has emerged as an important technique for accelerating the auto-regressive generation process of large language models (LLMs) by utilizing a draft-then-verify mechanism to produce multiple tokens per forward pass. While state-of-the-art speculative sampling methods use only a single layer and a language modeling (LM) head as the draft model to achieve impressive layer compression, their efficiency gains are substantially reduced for large-vocabulary LLMs, such as Llama-3-8B with a vocabulary of 128k tokens. To address this, we present FR-Spec, a frequency-ranked speculative sampling framework that optimizes draft candidate selection through vocabulary space compression. By constraining the draft search to a frequency-prioritized token subset, our method reduces LM Head computation overhead by 75% while ensuring the equivalence of the final output distribution. Experiments across multiple datasets demonstrate an average of 1.12\times speedup over the state-of-the-art speculative sampling method EAGLE-2. Code is availableat https://github.com/thunlp/FR-Spec.</abstract>
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%0 Conference Proceedings
%T FR-Spec: Accelerating Large-Vocabulary Language Models via Frequency-Ranked Speculative Sampling
%A Zhao, Weilin
%A Pan, Tengyu
%A Han, Xu
%A Zhang, Yudi
%A Sun, Ao
%A Huang, Yuxiang
%A Zhang, Kaihuo
%A Zhao, Weilun
%A Li, Yuxuan
%A Zhou, Jie
%A Zhou, Hao
%A Wang, Jianyong
%A Liu, Zhiyuan
%A Sun, Maosong
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F zhao-etal-2025-fr
%X Speculative sampling has emerged as an important technique for accelerating the auto-regressive generation process of large language models (LLMs) by utilizing a draft-then-verify mechanism to produce multiple tokens per forward pass. While state-of-the-art speculative sampling methods use only a single layer and a language modeling (LM) head as the draft model to achieve impressive layer compression, their efficiency gains are substantially reduced for large-vocabulary LLMs, such as Llama-3-8B with a vocabulary of 128k tokens. To address this, we present FR-Spec, a frequency-ranked speculative sampling framework that optimizes draft candidate selection through vocabulary space compression. By constraining the draft search to a frequency-prioritized token subset, our method reduces LM Head computation overhead by 75% while ensuring the equivalence of the final output distribution. Experiments across multiple datasets demonstrate an average of 1.12\times speedup over the state-of-the-art speculative sampling method EAGLE-2. Code is availableat https://github.com/thunlp/FR-Spec.
%R 10.18653/v1/2025.acl-long.198
%U https://aclanthology.org/2025.acl-long.198/
%U https://doi.org/10.18653/v1/2025.acl-long.198
%P 3909-3921
Markdown (Informal)
[FR-Spec: Accelerating Large-Vocabulary Language Models via Frequency-Ranked Speculative Sampling](https://aclanthology.org/2025.acl-long.198/) (Zhao et al., ACL 2025)
ACL
- Weilin Zhao, Tengyu Pan, Xu Han, Yudi Zhang, Ao Sun, Yuxiang Huang, Kaihuo Zhang, Weilun Zhao, Yuxuan Li, Jie Zhou, Hao Zhou, Jianyong Wang, Zhiyuan Liu, and Maosong Sun. 2025. FR-Spec: Accelerating Large-Vocabulary Language Models via Frequency-Ranked Speculative Sampling. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3909–3921, Vienna, Austria. Association for Computational Linguistics.