| title | KVarN: Variance-Normalized KV-Cache Quantization Mitigates Error Accumulation in Reasoning Tasks |
|---|---|
| authors | Lorenz K. Muller, Philippe Bich, Chiara Boretti, Hyun-Min Chang, Jiawei Zhuang, Lukas Cavigelli |
| date | 2026-06-02 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2606.03458 |
| affiliation | HUAWEI Computing Systems Lab |
| upvotes | 61 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Test-time scaling is a powerful approach to obtain better reasoning in large language models, but it becomes memory-bottlenecked during long-horizon decoding, as the KV-cache grows. KV-cache quantization can help improve this, but current methods are evaluated under prefill-like settings and errors behave differently under autoregressive decoding. We show that in the latter regime, quantization errors accumulate across timesteps, driven primarily by incorrect token scales. We introduce KVarN, a calibration-free KV-cache quantizer that applies a Hadamard rotation followed by a dual-scaling variance normalization across both axes of the K and V matrices. We find that this combination fixes outlying token-scale errors and substantially reduces error accumulation over existing baselines. KVarN establishes a new state-of-theart for KV-cache quantization on generative benchmarks, including MATH500, AIME24 and HumanEval, at 2-bit precision. A vLLM implementation of the KVarN method is available at https://github.com/huawei-csl/KVarN |
cite
@misc{muller2026kvarn,
title={KVarN: Variance-Normalized KV-Cache Quantization Mitigates Error Accumulation in Reasoning Tasks},
author={Lorenz K. Muller and Philippe Bich and Chiara Boretti and Hyun-Min Chang and Jiawei Zhuang and Lukas Cavigelli},
year={2026},
eprint={2606.03458},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2606.03458}
}