Low-Rank Adaptation papers, 2023–2026

Key findings

• Low-Rank Adaptation went from 282 papers in 2023 to 841 in 2026, roughly 3.0x in three years.
• NeurIPS publishes the most Low-Rank Adaptation work: 781 papers from 2023 to 2026, 34% of the four-venue total.
• The fastest-growing overlap is with Diffusion Models: papers tagged with both rose from 4 in 2023 to 61 in 2026.
• Among the 5 topics under Optimization and Scaling, it ranks #1 by 2026 paper count; the fastest grower in the group, Scaling Laws, is up 4.1x versus 3.0x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026$\delta$-Mem: : Efficient Online Memory For Large Language Models
• ICML 2026A3: an Analytical Low-Rank Approximation Framework for Attention
• ICLR 2026A Guardrail for Safety Preservation: When Safety-Sensitive Subspace Meets Harmful-Resistant Null-Space
• CVPR 20262-Shots in the Dark Low-Light Denoising with Minimal Data Acquisition
• NeurIPS 2025A geometric framework for momentum-based optimizers for low-rank training
• ICML 2025Beyond Low-rank Decomposition: A Shortcut Approach for Efficient On-Device Learning
• ICLR 2025A Quantum Circuit-Based Compression Perspective for Parameter-Efficient Learning
• CVPR 2025AdaDARE-gamma: Balancing Stability and Plasticity in Multi-modal LLMs through Efficient Adaptation
• NeurIPS 2024Bridging The Gap between Low-rank and Orthogonal Adaptation via Householder Reflection Adaptation
• ICML 2024A Tensor Decomposition Perspective on Second-order RNNs
• ICLR 2024A Hard-to-Beat Baseline for Training-free CLIP-based Adaptation
• CVPR 2024DePT: Decoupled Prompt Tuning
• NeurIPS 2023A Theoretical Analysis of the Test Error of Finite-Rank Kernel Ridge Regression
• ICML 2023A Three-regime Model of Network Pruning
• ICLR 2023A General Rank Preserving Framework for Asymmetric Image Retrieval
• CVPR 20231% VS 100%: Parameter-Efficient Low Rank Adapter for Dense Predictions
• NeurIPS 2026A low-rank decoder bottleneck bounds reliability in foundation-model perturbation prediction
• ICML 2026Adaptive Utilization of Low-Rank Adaptation via Conditioned Gating
• ICLR 2026ABBA-Adapters: Efficient and Expressive Fine-Tuning of Foundation Models
• CVPR 2026Basis-Oriented Low-rank Transfer for Few-Shot and Test-Time Adaptation

All Low-Rank Adaptation papers

518 papers, page 4 of 11

151. ICLR 2024Learning Hierarchical Polynomials with Three-Layer Neural Networks
152. ICLR 2024Learning Semantic Proxies from Visual Prompts for Parameter-Efficient Fine-Tuning in Deep Metric Learning
153. ICLR 2024Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Control
154. ICLR 2024LLaMA-Adapter: Efficient Fine-tuning of Large Language Models with Zero-initialized Attention
155. ICLR 2024LMUFormer: Low Complexity Yet Powerful Spiking Model With Legendre Memory Units
156. ICLR 2024LoftQ: LoRA-Fine-Tuning-aware Quantization for Large Language Models
157. ICLR 2024LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models
158. ICLR 2024Low Rank Matrix Completion via Robust Alternating Minimization in Nearly Linear Time
159. ICLR 2024LQ-LoRA: Low-rank plus Quantized Matrix Decomposition for Efficient Language Model Finetuning
160. ICLR 2024Memory-Assisted Sub-Prototype Mining for Universal Domain Adaptation
161. ICLR 2024Mixture of LoRA Experts
162. ICLR 2024Navigating Text-To-Image Customization: From LyCORIS Fine-Tuning to Model Evaluation
163. ICLR 2024Nemesis: Normalizing the Soft-prompt Vectors of Vision-Language Models
164. ICLR 2024NOLA: Compressing LoRA using Linear Combination of Random Basis
165. ICLR 2024OctoPack: Instruction Tuning Code Large Language Models
166. ICLR 2024Optimal Sketching for Residual Error Estimation for Matrix and Vector Norms
167. ICLR 2024OVOR: OnePrompt with Virtual Outlier Regularization for Rehearsal-Free Class-Incremental Learning
168. ICLR 2024PAC-FNO: Parallel-Structured All-Component Fourier Neural Operators for Recognizing Low-Quality Images
169. ICLR 2024Parameter-Efficient Multi-Task Model Fusion with Partial Linearization
170. ICLR 2024Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization
171. ICLR 2024Principled Federated Domain Adaptation: Gradient Projection and Auto-Weighting
172. ICLR 2024Project and Probe: Sample-Efficient Adaptation by Interpolating Orthogonal Features
173. ICLR 2024Provable Reward-Agnostic Preference-Based Reinforcement Learning
174. ICLR 2024Provably Efficient CVaR RL in Low-rank MDPs
175. ICLR 2024Provably Efficient UCB-type Algorithms For Learning Predictive State Representations
176. ICLR 2024Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning
177. ICLR 2024QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
178. ICLR 2024QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models
179. ICLR 2024ReLoRA: High-Rank Training Through Low-Rank Updates
180. ICLR 2024Rethinking Channel Dimensions to Isolate Outliers for Low-bit Weight Quantization of Large Language Models
181. ICLR 2024Sample-Efficient Quality-Diversity by Cooperative Coevolution
182. ICLR 2024Set Learning for Accurate and Calibrated Models
183. ICLR 2024SF(DA)$^2$: Source-free Domain Adaptation Through the Lens of Data Augmentation
184. ICLR 2024SPTNet: An Efficient Alternative Framework for Generalized Category Discovery with Spatial Prompt Tuning
185. ICLR 2024Statistically Optimal $K$-means Clustering via Nonnegative Low-rank Semidefinite Programming
186. ICLR 2024TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models
187. ICLR 2024Test-time Adaptation against Multi-modal Reliability Bias
188. ICLR 2024The Expressive Power of Low-Rank Adaptation
189. ICLR 2024The Hedgehog & the Porcupine: Expressive Linear Attentions with Softmax Mimicry
190. ICLR 2024The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction
191. ICLR 2024Towards Few-Shot Adaptation of Foundation Models via Multitask Finetuning
192. ICLR 2024Towards Robust and Efficient Cloud-Edge Elastic Model Adaptation via Selective Entropy Distillation
193. ICLR 2024Towards Seamless Adaptation of Pre-trained Models for Visual Place Recognition
194. ICLR 2024True Knowledge Comes from Practice: Aligning Large Language Models with Embodied Environments via Reinforcement Learning
195. ICLR 2024Tuning LayerNorm in Attention: Towards Efficient Multi-Modal LLM Finetuning
196. ICLR 2024UniAdapter: Unified Parameter-Efficient Transfer Learning for Cross-modal Modeling
197. ICLR 2024VeRA: Vector-based Random Matrix Adaptation
198. ICLR 2024ViDA: Homeostatic Visual Domain Adapter for Continual Test Time Adaptation
199. ICLR 2024When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method
200. ICLR 2024YaRN: Efficient Context Window Extension of Large Language Models

FAQ

How many Low-Rank Adaptation papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

2,273 in total: 282 in 2023, 518 in 2024, 632 in 2025, 841 in 2026.

How many Low-Rank Adaptation papers are at NeurIPS 2026?

NeurIPS 2026 has 269 Low-Rank Adaptation papers, versus 241 the year before.

How many Low-Rank Adaptation papers are at ICLR 2026?

ICLR 2026 has 184 Low-Rank Adaptation papers, versus 152 the year before.

How many Low-Rank Adaptation papers are at ICML 2026?

ICML 2026 has 240 Low-Rank Adaptation papers, versus 136 the year before.

How many Low-Rank Adaptation papers are at CVPR 2026?

CVPR 2026 has 148 Low-Rank Adaptation papers, versus 103 the year before.

Which conference publishes the most Low-Rank Adaptation papers?

NeurIPS, with 781 papers from 2023 to 2026.