arXiv AI By Yaoyang Liu, Shuwei Shen, Jincheng Tang, Jinguang Liu, Jinbao Xue, Hailong Yang, Ke Zhang, Yinjun Wu

BELT : BEhavior-Aligned Low-Rank KV Cache Compression with Learned Top-k Rank Allocation

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arXiv Machine Learning
Sep 25

A JoLT for the KV cache: Near-Lossless KV Cache Compression via Joint Rank-bit Allocation

The paper introduces JoLT, a training‑free compressor that jointly allocates rank and precision for key‑value (KV) cache compression in long‑context language models. JoLT treats grouped prefill caches as fourth‑order tensors, applies partial Tucker decomposition along token and feature modes, and uses a rotated low‑bit quantizer for residuals, all governed by a single Lagrangian dual under a global byte constraint. Across five models from four architecture families, JoLT achieves 2–3× compression with less than 0.2% perplexity loss, and near‑lossless retrieval accuracy on LLaMA‑3.1‑8B at 64K context up to 3× compression.

By Rahul Krishnan, Volker Schulz