[Submitted on 10 Aug 2026]
Title:To Keep or Not to Keep: Learning KV Cache Retention in Disaggregated LLM Serving Systems
View a PDF of the paper titled To Keep or Not to Keep: Learning KV Cache Retention in Disaggregated LLM Serving Systems, by Dong Liu and Yanxuan Yu and Eric Jiang and Shu Wang and Ying Nian Wu
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Abstract:Disaggregated \textsc{LLM} serving separates prefill and decode across node pools, placing a network fabric between \textsc{KV}-cache computation and consumption. This breaks a key assumption of classical cache policies: a miss no longer incurs only local recomputation, but also network transfer to the decode node. Likewise, admitting a block to the global cache requires an additional transfer, so poor retention decisions waste both memory and bandwidth. We present \textbf{KVLearn}, a learning-based framework for cost-aware \textsc{KV}-cache retention in disaggregated serving. KVLearn includes: (i) a lightweight Prefix Reuse Predictor (\textsc{PRP}) that estimates reuse probability from structural and temporal prefix features without modifying model weights; (ii) a Cost-Aware Retention Score (\textsc{CARS}) that converts reuse probability into keep/admit decisions using recomputation, transfer, and storage costs; and (iii) an Adaptive Threshold Controller (\textsc{ATC}) that adjusts admission thresholds online from observed hit rates and memory pressure. We integrate KVLearn into a globally disaggregated serving topology and evaluate it on text and multimodal workloads, including large image/video-derived \textsc{KV} blocks with heterogeneous reuse patterns. KVLearn reduces end-to-end time-to-first-token by up to 56% versus No-Cache, 38% versus LRU-Pool, and 33% versus Mooncake-style baselines, while cutting inter-node \textsc{KV} transfer volume by up to 53% versus LRU-Pool. On MM-Session, throughput remains within ${\sim}5%$ of oracle. Code is available at this https URL.
Comments: Accepted to ACM SYSTOR 2026
Subjects:
Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC)
Cite as: arXiv:2610.28637 [cs.LG]
(or arXiv:2610.28637v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.28637
arXiv-issued DOI via DataCite
Submission history
From: Dong Liu [view email] [v1] Mon, 10 Aug 2026 10:46:26 UTC (89 KB)
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