# LKV: End-to-End Learning of Head-wise Budgets and Token Selection for LLM KV Cache Eviction

Published: 2026-05-11 · Updated: 2026-05-11 · Category: Other

Source page: https://www.sipply.dev/story/lkv-end-to-end-learning-of-head-wise-budgets-and-token-272a8cc9 · 中文: https://www.sipply.dev/zh/story/lkv-end-to-end-learning-of-head-wise-budgets-and-token-272a8cc9

## Summary

Recent advancements in optimizing Key-Value (KV) cache mechanisms for Large Language Models (LLMs) have been highlighted through two innovative approaches: LKV and RateQuant. LKV, or Learned KV Eviction, presents a method that treats KV cache memory optimization as an end-to-end differentiable problem, outperforming traditional heuristic methods and achieving state-of-the-art results in cache retention. On the other hand, RateQuant focuses on mixed-precision quantization of KV caches, leveraging rate-distortion theory to minimize performance distortion while enhancing efficiency during model calibration and inference. Together, these methods represent significant strides in improving the operational efficiency of LLMs, particularly in managing memory resources effectively.

## Key takeaways

- LKV introduces a novel end-to-end approach for optimizing KV cache eviction in LLMs.
- RateQuant enhances mixed-precision quantization, reducing performance distortion in KV caches.
- Both methods contribute to improved efficiency and performance in large language models.

## Why it matters

These advancements are crucial as they enhance the efficiency and performance of large language models, which are increasingly integral to various applications in AI and machine learning.

- **Product managers:** For product managers, understanding these innovations can inform decisions on resource allocation and performance optimization for AI-driven products, ensuring they remain competitive.
- **Developers:** Developers and engineers can leverage these techniques to optimize LLM implementations, improving both memory management and processing efficiency in their applications.
- **Students and job seekers:** Students and job-seekers should note these developments as they highlight emerging trends in AI research, providing insights into potential career paths and areas of expertise in machine learning and model optimization.

## Sources

- [LKV: End-to-End Learning of Head-wise Budgets and Token Selection for LLM KV Cache Eviction](https://arxiv.org/abs/2605.06676) (arXiv cs.LG, 2026-05-11)
- [RateQuant: Optimal Mixed-Precision KV Cache Quantization via Rate-Distortion Theory](https://arxiv.org/abs/2605.06675) (arXiv cs.LG, 2026-05-11)

## 中文

### LKV：针对 LLM KV 缓存驱逐的头部预算和令牌选择的端到端学习

最近在优化大型语言模型（LLM）的键值（KV）缓存机制方面取得了进展，提出了两种创新方法：LKV 和 RateQuant。LKV（学习的 KV 驱逐）将 KV 缓存内存优化视为一个端到端可微分的问题，超越了传统启发式方法，并在缓存保留方面取得了最先进的结果。另一方面，RateQuant 专注于 KV 缓存的混合精度量化，利用率失真理论在模型校准和推理过程中最小化性能失真，同时提高效率。这两种方法在有效管理内存资源方面显著提升了 LLM 的操作效率。

**要点：**

- LKV 引入了一种新颖的端到端方法来优化 LLM 中的 KV 缓存驱逐。
- RateQuant 提升了混合精度量化，减少了 KV 缓存中的性能失真。
- 这两种方法共同促进了大型语言模型的效率和性能提升。

**为什么重要：** 这些进展至关重要，因为它们提高了大型语言模型的效率和性能，而大型语言模型在人工智能和机器学习的各种应用中变得越来越重要。

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