@inproceedings{chen-etal-2025-clasp,
title = "{CL}a{S}p: In-Context Layer Skip for Self-Speculative Decoding",
author = "Chen, Longze and
Shan, Renke and
Wang, Huiming and
Wang, Lu and
Liu, Ziqiang and
Luo, Run and
Wang, Jiawei and
Alinejad-Rokny, Hamid and
Yang, Min",
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.1525/",
doi = "10.18653/v1/2025.acl-long.1525",
pages = "31608--31618",
ISBN = "979-8-89176-251-0",
abstract = "Speculative decoding (SD) is a promising method for accelerating the decoding process of Large Language Models (LLMs). The efficiency of SD primarily hinges on the consistency between the draft model and the verify model. However, existing drafting approaches typically require additional modules to be trained, which can be challenging to implement and ensure compatibility across various LLMs. In this paper, we propose CLaSp, an in-context layer-skipping strategy for self-speculative decoding. Unlike prior methods, CLaSp does not require additional drafting modules or extra training. Instead, it employs a plug-and-play mechanism by skipping intermediate layers of the verify model to construct a compressed draft model. Specifically, we develop a dynamic programming algorithm that optimizes the layer-skipping process by leveraging the complete hidden states from the last verification stage as an objective. This enables CLaSp to dynamically adjust its layer-skipping strategy after each verification stage, without relying on pre-optimized sets of skipped layers. Experimental results across diverse downstream tasks demonstrate that CLaSp achieves a speedup of $1.3\times \sim 1.7\times$ on LLaMA3 series models without altering the original distribution of the generated text."
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<abstract>Speculative decoding (SD) is a promising method for accelerating the decoding process of Large Language Models (LLMs). The efficiency of SD primarily hinges on the consistency between the draft model and the verify model. However, existing drafting approaches typically require additional modules to be trained, which can be challenging to implement and ensure compatibility across various LLMs. In this paper, we propose CLaSp, an in-context layer-skipping strategy for self-speculative decoding. Unlike prior methods, CLaSp does not require additional drafting modules or extra training. Instead, it employs a plug-and-play mechanism by skipping intermediate layers of the verify model to construct a compressed draft model. Specifically, we develop a dynamic programming algorithm that optimizes the layer-skipping process by leveraging the complete hidden states from the last verification stage as an objective. This enables CLaSp to dynamically adjust its layer-skipping strategy after each verification stage, without relying on pre-optimized sets of skipped layers. Experimental results across diverse downstream tasks demonstrate that CLaSp achieves a speedup of 1.3\times \sim 1.7\times on LLaMA3 series models without altering the original distribution of the generated text.</abstract>
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%0 Conference Proceedings
%T CLaSp: In-Context Layer Skip for Self-Speculative Decoding
%A Chen, Longze
%A Shan, Renke
%A Wang, Huiming
%A Wang, Lu
%A Liu, Ziqiang
%A Luo, Run
%A Wang, Jiawei
%A Alinejad-Rokny, Hamid
%A Yang, Min
%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 chen-etal-2025-clasp
%X Speculative decoding (SD) is a promising method for accelerating the decoding process of Large Language Models (LLMs). The efficiency of SD primarily hinges on the consistency between the draft model and the verify model. However, existing drafting approaches typically require additional modules to be trained, which can be challenging to implement and ensure compatibility across various LLMs. In this paper, we propose CLaSp, an in-context layer-skipping strategy for self-speculative decoding. Unlike prior methods, CLaSp does not require additional drafting modules or extra training. Instead, it employs a plug-and-play mechanism by skipping intermediate layers of the verify model to construct a compressed draft model. Specifically, we develop a dynamic programming algorithm that optimizes the layer-skipping process by leveraging the complete hidden states from the last verification stage as an objective. This enables CLaSp to dynamically adjust its layer-skipping strategy after each verification stage, without relying on pre-optimized sets of skipped layers. Experimental results across diverse downstream tasks demonstrate that CLaSp achieves a speedup of 1.3\times \sim 1.7\times on LLaMA3 series models without altering the original distribution of the generated text.
%R 10.18653/v1/2025.acl-long.1525
%U https://aclanthology.org/2025.acl-long.1525/
%U https://doi.org/10.18653/v1/2025.acl-long.1525
%P 31608-31618
Markdown (Informal)
[CLaSp: In-Context Layer Skip for Self-Speculative Decoding](https://aclanthology.org/2025.acl-long.1525/) (Chen et al., ACL 2025)
ACL
- Longze Chen, Renke Shan, Huiming Wang, Lu Wang, Ziqiang Liu, Run Luo, Jiawei Wang, Hamid Alinejad-Rokny, and Min Yang. 2025. CLaSp: In-Context Layer Skip for Self-Speculative Decoding. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 31608–31618, Vienna, Austria. Association for Computational Linguistics.