SCOPE: Optimizing Key-Value Cache Compression in Long-context Generation

Jialong Wu, Zhenglin Wang, Linhai Zhang, Yilong Lai, Yulan He, Deyu Zhou


Abstract
Key-Value (KV) cache has become a bottleneck of LLMs for long-context generation. Despite the numerous efforts in this area, the optimization for the decoding phase is generally ignored. However, we believe such optimization is crucial, especially for long-output generation tasks based on the following two observations: (i) Excessive compression during the prefill phase, which requires specific full context impairs the comprehension of the reasoning task; (ii) Deviation of heavy hitters occurs in the reasoning tasks with long outputs. Therefore, SCOPE, a simple yet efficient framework that separately performs KV cache optimization during the prefill and decoding phases, is introduced. Specifically, the KV cache during the prefill phase is preserved to maintain the essential information, while a novel strategy based on sliding is proposed to select essential heavy hitters for the decoding phase. Memory usage and memory transfer are further optimized using adaptive and discontinuous strategies. Extensive experiments on LongGenBench show the effectiveness and generalization of SCOPE and its compatibility as a plug-in to other prefill-only KV compression methods.
Anthology ID:
2025.acl-long.529
Volume:
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2025
Address:
Vienna, Austria
Editors:
Wanxiang Che, Joyce Nabende, Ekaterina Shutova, Mohammad Taher Pilehvar
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
10775–10790
Language:
URL:
https://preview.aclanthology.org/landing_page/2025.acl-long.529/
DOI:
Bibkey:
Cite (ACL):
Jialong Wu, Zhenglin Wang, Linhai Zhang, Yilong Lai, Yulan He, and Deyu Zhou. 2025. SCOPE: Optimizing Key-Value Cache Compression in Long-context Generation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 10775–10790, Vienna, Austria. Association for Computational Linguistics.
Cite (Informal):
SCOPE: Optimizing Key-Value Cache Compression in Long-context Generation (Wu et al., ACL 2025)
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PDF:
https://preview.aclanthology.org/landing_page/2025.acl-long.529.pdf