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 7 of 11

301. ICML 2024Random features models: a way to study the success of naive imputation
302. ICML 2024Random Masking Finds Winning Tickets for Parameter Efficient Fine-tuning
303. ICML 2024Recovering the Pre-Fine-Tuning Weights of Generative Models
304. ICML 2024Reducing Fine-Tuning Memory Overhead by Approximate and Memory-Sharing Backpropagation
305. ICML 2024Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark
306. ICML 2024Reweighted Solutions for Weighted Low Rank Approximation
307. ICML 2024Riemannian Preconditioned LoRA for Fine-Tuning Foundation Models
308. ICML 2024RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation
309. ICML 2024Sample-Efficient Multiagent Reinforcement Learning with Reset Replay
310. ICML 2024Self-attention Networks Localize When QK-eigenspectrum Concentrates
311. ICML 2024SeMOPO: Learning High-quality Model and Policy from Low-quality Offline Visual Datasets
312. ICML 2024SFC: Achieve Accurate Fast Convolution under Low-precision Arithmetic
313. ICML 2024Sign Rank Limitations for Inner Product Graph Decoders
314. ICML 2024Simplicity Bias via Global Convergence of Sharpness Minimization
315. ICML 2024Sparse is Enough in Fine-tuning Pre-trained Large Language Models
316. ICML 2024SPP: Sparsity-Preserved Parameter-Efficient Fine-Tuning for Large Language Models
317. ICML 2024SuDA: Support-based Domain Adaptation for Sim2Real Hinge Joint Tracking with Flexible Sensors
318. ICML 2024Symmetric Matrix Completion with ReLU Sampling
319. ICML 2024TabLog: Test-Time Adaptation for Tabular Data Using Logic Rules
320. ICML 2024Testing the Feasibility of Linear Programs with Bandit Feedback
321. ICML 2024Towards efficient deep spiking neural networks construction with spiking activity based pruning
322. ICML 2024Trainable Transformer in Transformer
323. ICML 2024Unsupervised Parameter-free Simplicial Representation Learning with Scattering Transforms
324. ICML 2024Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models
325. ICML 2024Which Frequencies do CNNs Need? Emergent Bottleneck Structure in Feature Learning
326. NeurIPS 2024$\textit{Trans-LoRA}$: towards data-free Transferable Parameter Efficient Finetuning
327. NeurIPS 20242DQuant: Low-bit Post-Training Quantization for Image Super-Resolution
328. NeurIPS 20243-in-1: 2D Rotary Adaptation for Efficient Finetuning, Efficient Batching and Composability
329. NeurIPS 2024A Bayesian Approach to Data Point Selection
330. NeurIPS 2024A Geometric View of Data Complexity: Efficient Local Intrinsic Dimension Estimation with Diffusion Models
331. NeurIPS 2024A Swiss Army Knife for Heterogeneous Federated Learning: Flexible Coupling via Trace Norm
332. NeurIPS 2024Abrupt Learning in Transformers: A Case Study on Matrix Completion
333. NeurIPS 2024Adapting to Unknown Low-Dimensional Structures in Score-Based Diffusion Models
334. NeurIPS 2024An Offline Adaptation Framework for Constrained Multi-Objective Reinforcement Learning
335. NeurIPS 2024AUC Maximization under Positive Distribution Shift
336. NeurIPS 2024BAdam: A Memory Efficient Full Parameter Optimization Method for Large Language Models
337. NeurIPS 2024Be Confident in What You Know: Bayesian Parameter Efficient Fine-Tuning of Vision Foundation Models
338. NeurIPS 2024BeanCounter: A low-toxicity, large-scale, and open dataset of business-oriented text
339. NeurIPS 2024Beating Adversarial Low-Rank MDPs with Unknown Transition and Bandit Feedback
340. NeurIPS 2024BitsFusion: 1.99 bits Weight Quantization of Diffusion Model
341. NeurIPS 2024BLAST: Block-Level Adaptive Structured Matrices for Efficient Deep Neural Network Inference
342. NeurIPS 2024BLoB: Bayesian Low-Rank Adaptation by Backpropagation for Large Language Models
343. NeurIPS 2024BOLD: Boolean Logic Deep Learning
344. NeurIPS 2024Bounds for the smallest eigenvalue of the NTK for arbitrary spherical data of arbitrary dimension
345. NeurIPS 2024Bridging The Gap between Low-rank and Orthogonal Adaptation via Householder Reflection Adaptation
346. NeurIPS 2024Building on Efficient Foundations: Effective Training of LLMs with Structured Feedforward Layers
347. NeurIPS 2024Cherry on Top: Parameter Heterogeneity and Quantization in Large Language Models
348. NeurIPS 2024Coherence-free Entrywise Estimation of Eigenvectors in Low-rank Signal-plus-noise Matrix Models
349. NeurIPS 2024CoMERA: Computing- and Memory-Efficient Training via Rank-Adaptive Tensor Optimization
350. NeurIPS 2024Communication Efficient Distributed Training with Distributed Lion

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.