Scaling Laws papers, 2023–2026
Data and model scaling
Scope: NeurIPS, ICLR, ICML and CVPR, 2023–2026. 605 papers are tagged with this topic.
Key findings
- Scaling Laws went from 60 papers in 2023 to 248 in 2026, roughly 4.1x in three years.
- NeurIPS publishes the most Scaling Laws work: 221 papers from 2023 to 2026, 37% of the four-venue total.
- The fastest-growing overlap is with Time Series: papers tagged with both rose from 1 in 2023 to 24 in 2026.
- Among the 5 topics under Optimization and Scaling, it ranks #5 by 2026 paper count; the fastest grower in the group, Low-Rank Adaptation, is up 3.0x versus 4.1x here.
Papers per venue
Counts by year and venue
| Year | NeurIPS | ICLR | ICML | CVPR | Total |
|---|---|---|---|---|---|
| 2023 | 21 | 14 | 17 | 8 | 60 |
| 2024 | 38 | 15 | 33 | 10 | 96 |
| 2025 | 80 | 48 | 56 | 17 | 201 |
| 2026 | 82 | 73 | 78 | 15 | 248 |
Representative papers
Spread across years and venues so every edition shows up
- NeurIPS 2026A Unified Merge Calculus for Learning-Rate Scaling on Neural Computation Graphs
- ICML 2026A Capacity-Based Rationale for Multi-Head Attention
- ICLR 2026$\mathbf{Li_2}$: A Framework on Dynamics of Feature Emergence and Delayed Generalization
- CVPR 2026A Unified Framework for Knowledge Transfer in Bidirectional Model Scaling
- NeurIPS 2025Audits Under Resource, Data, and Access Constraints: Scaling Laws For Less Discriminatory Alternatives
- ICML 2025A Simple Model of Inference Scaling Laws
- ICLR 2025(Mis)Fitting Scaling Laws: A Survey of Scaling Law Fitting Techniques in Deep Learning
- CVPR 2025D^3: Scaling Up Deepfake Detection by Learning from Discrepancy
- NeurIPS 2024An exactly solvable model for emergence and scaling laws in the multitask sparse parity problem
- ICML 2024A Dynamical Model of Neural Scaling Laws
- ICLR 2024Principled Architecture-aware Scaling of Hyperparameters
- CVPR 2024From Activation to Initialization: Scaling Insights for Optimizing Neural Fields
- NeurIPS 2023Abide by the law and follow the flow: conservation laws for gradient flows
- ICML 2023Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning
- ICLR 2023Distributionally Robust Post-hoc Classifiers under Prior Shifts
- CVPR 2023LargeKernel3D: Scaling Up Kernels in 3D Sparse CNNs
- NeurIPS 2026ADIS-Law: Unified Scaling Laws for Annealing-Phase Domain Injection in Large Language Models
- ICML 2026Balancing Learning Rates Across Layers: Exact Two-Step Dynamics and Optimal Scaling in Linear Neural Networks
- ICLR 2026A universal compression theory: Lottery ticket hypothesis and superpolynomial scaling laws
- CVPR 2026MatAnyone 2 Scaling Video Matting via a Learned Quality Evaluator
All Scaling Laws papers
605 papers, page 12 of 13
- CVPR 2023ResFormer: Scaling ViTs With Multi-Resolution Training
- CVPR 2023Scaling Language-Image Pre-Training via Masking
- CVPR 2023VideoMAE V2: Scaling Video Masked Autoencoders With Dual Masking
- ICLR 2023Block and Subword-Scaling Floating-Point (BSFP) : An Efficient Non-Uniform Quantization For Low Precision Inference
- ICLR 2023Broken Neural Scaling Laws
- ICLR 2023Distributionally Robust Post-hoc Classifiers under Prior Shifts
- ICLR 2023How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization
- ICLR 2023Massively Scaling Heteroscedastic Classifiers
- ICLR 2023More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity
- ICLR 2023Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks
- ICLR 2023Reparameterization through Spatial Gradient Scaling
- ICLR 2023Scaling Forward Gradient With Local Losses
- ICLR 2023Scaling Laws For Deep Learning Based Image Reconstruction
- ICLR 2023Scaling up and Stabilizing Differentiable Planning with Implicit Differentiation
- ICLR 2023Scaling Up Probabilistic Circuits by Latent Variable Distillation
- ICLR 2023The Onset of Variance-Limited Behavior for Networks in the Lazy and Rich Regimes
- ICLR 2023Unsupervised Manifold Alignment with Joint Multidimensional Scaling
- ICML 2023Beyond the Universal Law of Robustness: Sharper Laws for Random Features and Neural Tangent Kernels
- ICML 2023Data Efficient Neural Scaling Law via Model Reusing
- ICML 2023Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning
- ICML 2023Hidden Symmetries of ReLU Networks
- ICML 2023Learning Physical Models that Can Respect Conservation Laws
- ICML 2023Long Horizon Temperature Scaling
- ICML 2023On the Convergence Rate of Gaussianization with Random Rotations
- ICML 2023Online Platt Scaling with Calibeating
- ICML 2023Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling
- ICML 2023Scaling Laws for Generative Mixed-Modal Language Models
- ICML 2023Scaling Laws for Multilingual Neural Machine Translation
- ICML 2023Scaling Laws for Reward Model Overoptimization
- ICML 2023Scaling of Class-wise Training Losses for Post-hoc Calibration
- ICML 2023Scaling Spherical CNNs
- ICML 2023Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory
- ICML 2023TAN Without a Burn: Scaling Laws of DP-SGD
- ICML 2023The case for 4-bit precision: k-bit Inference Scaling Laws
- NeurIPS 2023A Fast and Accurate Estimator for Large Scale Linear Model via Data Averaging
- NeurIPS 2023Abide by the law and follow the flow: conservation laws for gradient flows
- NeurIPS 2023DynaDojo: An Extensible Benchmarking Platform for Scalable Dynamical System Identification
- NeurIPS 2023Energy-Efficient Scheduling with Predictions
- NeurIPS 2023Focused Transformer: Contrastive Training for Context Scaling
- NeurIPS 2023Getting ViT in Shape: Scaling Laws for Compute-Optimal Model Design
- NeurIPS 2023Hierarchical clustering with dot products recovers hidden tree structure
- NeurIPS 2023Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition
- NeurIPS 2023Neural Data Transformer 2: Multi-context Pretraining for Neural Spiking Activity
- NeurIPS 2023Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sources
- NeurIPS 2023R-divergence for Estimating Model-oriented Distribution Discrepancy
- NeurIPS 2023SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Model
- NeurIPS 2023ScaleLong: Towards More Stable Training of Diffusion Model via Scaling Network Long Skip Connection
- NeurIPS 2023Scaling Data-Constrained Language Models
- NeurIPS 2023Scaling laws for language encoding models in fMRI
- NeurIPS 2023Scaling MLPs: A Tale of Inductive Bias
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FAQ
- How many Scaling Laws papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?
- 605 in total: 60 in 2023, 96 in 2024, 201 in 2025, 248 in 2026.
- How many Scaling Laws papers are at NeurIPS 2026?
- NeurIPS 2026 has 82 Scaling Laws papers, versus 80 the year before.
- How many Scaling Laws papers are at ICLR 2026?
- ICLR 2026 has 73 Scaling Laws papers, versus 48 the year before.
- How many Scaling Laws papers are at ICML 2026?
- ICML 2026 has 78 Scaling Laws papers, versus 56 the year before.
- How many Scaling Laws papers are at CVPR 2026?
- CVPR 2026 has 15 Scaling Laws papers, versus 17 the year before.
- Which conference publishes the most Scaling Laws papers?
- NeurIPS, with 221 papers from 2023 to 2026.