#SVD & Low-Rank

2026

• 06-26 EN

  SigmaScale: Learning to Scale Weight Matrices for Better SVD-Based LLM Compression

• 06-26 中

  SigmaScale 阅读笔记：通过学习缩放矩阵改进 SVD 大语言模型压缩

• 06-19 EN

  LASER: How Throwing Away 99% of a Weight Matrix Can Make LLMs Smarter

• 06-19 中

  LASER：丢掉 99% 的矩阵秩，LLM 推理准确率反而提高了 27%

• 06-12 EN

  SliceGPT: Post-Training LLM Compression via Computational Invariance

• 06-12 中

  SliceGPT 阅读笔记：用计算不变性删除 Transformer 的行与列

• 05-29 EN

  IO-SVD: Input-Output Whitened SVD for Adaptive-Rank LLM Compression

• 05-29 中

  IO-SVD：基于输入输出双侧白化的自适应秩LLM压缩方法

• 05-22 EN

  DoRA: Weight-Decomposed Low-Rank Adaptation — Technical Review

• 05-22 中

  DoRA：权重分解低秩自适应——用幅度与方向解耦提升 LoRA 学习能力 | 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

• 05-08 EN

  Swift-SVD: Activation-Aware Low-Rank Compression for LLM Weights and KV Cache

• 05-01 EN

  Low-Rank Optimization Trajectories for LLM RLVR Acceleration: A Technical Review of NExt

• 04-10 EN

  SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression — Deep Technical Review

• 04-10 中

  SVD-LLM：面向大语言模型压缩的“截断感知”奇异值分解方法 — 深度阅读笔记

• 03-27 EN

  GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection — In-Depth Technical Review

• 03-23 EN

  MiRA: A Subgoal-driven Framework for Improving Long-Horizon LLM Agents — Technical Review

• 03-13 EN

  LoRA: Fine-Tuning Giant Models with Pocket Change — The Low-Rank Revolution