#Quantization

2026

• 09-17 EN

  QeRL: When Quantized Weights Help Reinforcement Learning, and Where the Evidence Stops

• 09-17 中

  QeRL 阅读笔记：量化、探索与训练效率

• 08-26 EN

  HqeKV: Making Quantization and Eviction Cooperate Inside a Single KV Cache Compression Framework

• 08-26 中

  HqeKV 阅读笔记：让量化和淘汰在一个 KV Cache 压缩框架里协同工作

• 08-19 EN

  KV Cache Compression Through the Lens of Transform Coding: What AATC Gets Right About Attention-Aware Quantization

• 08-19 中

  AATC 阅读笔记：用变换编码的视角重新理解 KV Cache 量化

• 08-08 EN

  QEvict: Recoverable Quantized KV Eviction for Attention-Drift-Robust Long-Context Decoding

• 08-08 中

  QEvict 阅读笔记：把 KV Cache 淘汰从「一锤子买卖」变成可逆的三层调度

• 07-24 EN

  SVD-Surgeon: Bringing Optimal Brain Surgeon to the Singular-Value Basis

• 07-24 中

  SVD-Surgeon：把「最优脑外科手术」搬进奇异值空间

• 07-16 EN

  GIFT: Why the Coordinate System You Quantize In Matters More Than the Quantizer

• 07-16 中

  GIFT 阅读笔记:量化用的坐标系,比量化器本身更重要

• 06-17 EN

  OScaR: Occam's Razor for Extreme KV Cache Quantization

• 06-17 中

  OScaR：极端 KV 缓存量化的奥卡姆剃刀

• 06-03 EN

  KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization — Technical Review

• 06-03 中

  KVQuant：面向千万级上下文的 KV 缓存量化技术——阅读笔记

• 05-15 EN

  Zero Sum SVD: A Global, Loss-Aware Rank Budget for LLM Compression

• 05-15 中

  Zero Sum SVD：用「损失零和」做全局奇异值预算分配的 LLM 压缩方法

• 04-24 EN

  Generalization at the Edge of Stability: A Random Dynamical Systems Perspective

• 04-08 EN

  SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models — In-Depth Technical Review

• 04-08 中

  SmoothQuant：大型语言模型的精准高效训练后量化 — 深度阅读笔记

• 04-03 EN

  AWQ: Activation-aware Weight Quantization for On-Device LLM Compression and Acceleration — In-Depth Technical Review

• 04-03 中

  AWQ：感知激活值的大模型权重量化压缩与加速 — 深度阅读笔记

• 03-25 EN

  GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers — In-Depth Technical Review

• 03-21 EN

  BitNet: Scaling 1-bit Transformers for Large Language Models — In-Depth Technical Review