R-KV: Redundancy-aware KV Cache Compression for Reasoning Models

• URL: http://arxiv.org/abs/2505.24133v3
• Date: Fri, 13 Jun 2025 21:01:43 GMT
• Title: R-KV: Redundancy-aware KV Cache Compression for Reasoning Models
• Authors: Zefan Cai, Wen Xiao, Hanshi Sun, Cheng Luo, Yikai Zhang, Ke Wan, Yucheng Li, Yeyang Zhou, Li-Wen Chang, Jiuxiang Gu, Zhen Dong, Anima Anandkumar, Abedelkadir Asi, Junjie Hu,
• Abstract summary: We propose Redundancy-aware KV Cache Compression for Reasoning models (R-KV)<n>R-KV preserves nearly 100% of the full KV cache performance using only 10% of the KV cache.<n>Remarkably, R-KV even achieves 105% of full KV cache performance with 16% of the KV cache.
• Score: 77.84539432982307
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reasoning models have demonstrated impressive performance in self-reflection and chain-of-thought reasoning. However, they often produce excessively long outputs, leading to prohibitively large key-value (KV) caches during inference. While chain-of-thought inference significantly improves performance on complex reasoning tasks, it can also lead to reasoning failures when deployed with existing KV cache compression approaches. To address this, we propose Redundancy-aware KV Cache Compression for Reasoning models (R-KV), a novel method specifically targeting redundant tokens in reasoning models. Our method preserves nearly 100% of the full KV cache performance using only 10% of the KV cache, substantially outperforming existing KV cache baselines, which reach only 60% of the performance. Remarkably, R-KV even achieves 105% of full KV cache performance with 16% of the KV cache. This KV-cache reduction also leads to a 90% memory saving and a 6.6X throughput over standard chain-of-thought reasoning inference. Experimental results show that R-KV consistently outperforms existing KV cache compression baselines across two mathematical reasoning datasets.

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