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

2,272 papers, page 33 of 46

1601. ICLR 2024DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
1602. ICLR 2024Debiasing Algorithm through Model Adaptation
1603. ICLR 2024DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning
1604. ICLR 2024Differentiable Learning of Generalized Structured Matrices for Efficient Deep Neural Networks
1605. ICLR 2024Dissecting learning and forgetting in language model finetuning
1606. ICLR 2024DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning
1607. ICLR 2024Dynamic Layer Tying for Parameter-Efficient Transformers
1608. ICLR 2024Early Neuron Alignment in Two-layer ReLU Networks with Small Initialization
1609. ICLR 2024EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion Models
1610. ICLR 2024Enhancing Neural Training via a Correlated Dynamics Model
1611. ICLR 2024Exploring the Common Appearance-Boundary Adaptation for Nighttime Optical Flow
1612. ICLR 2024FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in Medical Image Analysis
1613. ICLR 2024Federated Q-Learning: Linear Regret Speedup with Low Communication Cost
1614. ICLR 2024First-order ANIL provably learns representations despite overparametrisation
1615. ICLR 2024Gradual Domain Adaptation via Gradient Flow
1616. ICLR 2024Implicit bias of SGD in $L_2$-regularized linear DNNs: One-way jumps from high to low rank
1617. ICLR 2024Implicit Neural Representation Inference for Low-Dimensional Bayesian Deep Learning
1618. ICLR 2024Improving LoRA in Privacy-preserving Federated Learning
1619. ICLR 2024Improving Non-Transferable Representation Learning by Harnessing Content and Style
1620. ICLR 2024Increasing Model Capacity for Free: A Simple Strategy for Parameter Efficient Fine-tuning
1621. ICLR 2024Koopman-based generalization bound: New aspect for full-rank weights
1622. ICLR 2024Learning Conditional Invariances through Non-Commutativity
1623. ICLR 2024Learning Hierarchical Polynomials with Three-Layer Neural Networks
1624. ICLR 2024Learning Semantic Proxies from Visual Prompts for Parameter-Efficient Fine-Tuning in Deep Metric Learning
1625. ICLR 2024Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Control
1626. ICLR 2024LLaMA-Adapter: Efficient Fine-tuning of Large Language Models with Zero-initialized Attention
1627. ICLR 2024LMUFormer: Low Complexity Yet Powerful Spiking Model With Legendre Memory Units
1628. ICLR 2024LoftQ: LoRA-Fine-Tuning-aware Quantization for Large Language Models
1629. ICLR 2024LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models
1630. ICLR 2024Low Rank Matrix Completion via Robust Alternating Minimization in Nearly Linear Time
1631. ICLR 2024LQ-LoRA: Low-rank plus Quantized Matrix Decomposition for Efficient Language Model Finetuning
1632. ICLR 2024Memory-Assisted Sub-Prototype Mining for Universal Domain Adaptation
1633. ICLR 2024Mixture of LoRA Experts
1634. ICLR 2024Navigating Text-To-Image Customization: From LyCORIS Fine-Tuning to Model Evaluation
1635. ICLR 2024Nemesis: Normalizing the Soft-prompt Vectors of Vision-Language Models
1636. ICLR 2024NOLA: Compressing LoRA using Linear Combination of Random Basis
1637. ICLR 2024OctoPack: Instruction Tuning Code Large Language Models
1638. ICLR 2024Optimal Sketching for Residual Error Estimation for Matrix and Vector Norms
1639. ICLR 2024OVOR: OnePrompt with Virtual Outlier Regularization for Rehearsal-Free Class-Incremental Learning
1640. ICLR 2024PAC-FNO: Parallel-Structured All-Component Fourier Neural Operators for Recognizing Low-Quality Images
1641. ICLR 2024Parameter-Efficient Multi-Task Model Fusion with Partial Linearization
1642. ICLR 2024Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization
1643. ICLR 2024Principled Federated Domain Adaptation: Gradient Projection and Auto-Weighting
1644. ICLR 2024Project and Probe: Sample-Efficient Adaptation by Interpolating Orthogonal Features
1645. ICLR 2024Provable Reward-Agnostic Preference-Based Reinforcement Learning
1646. ICLR 2024Provably Efficient CVaR RL in Low-rank MDPs
1647. ICLR 2024Provably Efficient UCB-type Algorithms For Learning Predictive State Representations
1648. ICLR 2024Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning
1649. ICLR 2024QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
1650. ICLR 2024QLLM: Accurate and Efficient Low-Bitwidth Quantization for 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.