Scaling Laws papers, 2023–2026

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.

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

221 papers, page 4 of 5

151. NeurIPS 2025Scaling Unlocks Broader Generation and Deeper Functional Understanding of Proteins
152. NeurIPS 2025Scaling Up Active Testing to Large Language Models
153. NeurIPS 2025Sloth: scaling laws for LLM skills to predict multi-benchmark performance across families
154. NeurIPS 2025Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism
155. NeurIPS 2025Superposition Yields Robust Neural Scaling
156. NeurIPS 2025SWE-smith: Scaling Data for Software Engineering Agents
157. NeurIPS 2025TabDPT: Scaling Tabular Foundation Models on Real Data
158. NeurIPS 2025The Curse of Depth in Large Language Models
159. NeurIPS 2025Understanding LLM Behaviors via Compression: Data Generation, Knowledge Acquisition and Scaling Laws
160. NeurIPS 2025Unified Scaling Laws for Compressed Representations
161. NeurIPS 2025When Worse is Better: Navigating the Compression Generation Trade-off In Visual Tokenization
162. NeurIPS 2025Zero-Shot Performance Prediction for Probabilistic Scaling Laws
163. NeurIPS 2024$\texttt{Model-GLUE}$: Democratized LLM Scaling for A Large Model Zoo in the Wild
164. NeurIPS 20244+3 Phases of Compute-Optimal Neural Scaling Laws
165. NeurIPS 2024A Combinatorial Algorithm for the Semi-Discrete Optimal Transport Problem
166. NeurIPS 2024A Fast Convoluted Story: Scaling Probabilistic Inference for Integer Arithmetics
167. NeurIPS 2024An exactly solvable model for emergence and scaling laws in the multitask sparse parity problem
168. NeurIPS 2024ANAH-v2: Scaling Analytical Hallucination Annotation of Large Language Models
169. NeurIPS 2024Are More LLM Calls All You Need? Towards the Scaling Properties of Compound AI Systems
170. NeurIPS 2024Bigger, Regularized, Optimistic: scaling for compute and sample efficient continuous control
171. NeurIPS 2024Consent in Crisis: The Rapid Decline of the AI Data Commons
172. NeurIPS 2024D-CPT Law: Domain-specific Continual Pre-Training Scaling Law for Large Language Models
173. NeurIPS 2024Dimension-free deterministic equivalents and scaling laws for random feature regression
174. NeurIPS 2024fMRI predictors based on language models of increasing complexity recover brain left lateralization
175. NeurIPS 2024MAmmoTH2: Scaling Instructions from the Web
176. NeurIPS 2024MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens
177. NeurIPS 2024Model Collapse Demystified: The Case of Regression
178. NeurIPS 2024Neuc-MDS: Non-Euclidean Multidimensional Scaling Through Bilinear Forms
179. NeurIPS 2024On the Scalability of GNNs for Molecular Graphs
180. NeurIPS 2024Post-Hoc Reversal: Are We Selecting Models Prematurely?
181. NeurIPS 2024Repurposing Language Models into Embedding Models: Finding the Compute-Optimal Recipe
182. NeurIPS 2024Resolving Discrepancies in Compute-Optimal Scaling of Language Models
183. NeurIPS 2024Scaling Continuous Latent Variable Models as Probabilistic Integral Circuits
184. NeurIPS 2024Scaling Law for Time Series Forecasting
185. NeurIPS 2024Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations
186. NeurIPS 2024Scaling laws for learning with real and surrogate data
187. NeurIPS 2024Scaling Laws in Linear Regression: Compute, Parameters, and Data
188. NeurIPS 2024Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies
189. NeurIPS 2024Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained Transformers
190. NeurIPS 2024Scaling Retrieval-Based Language Models with a Trillion-Token Datastore
191. NeurIPS 2024Scaling the Codebook Size of VQ-GAN to 100,000 with a Utilization Rate of 99%
192. NeurIPS 2024Scaling transformer neural networks for skillful and reliable medium-range weather forecasting
193. NeurIPS 2024Scaling White-Box Transformers for Vision
194. NeurIPS 2024Searching for Efficient Linear Layers over a Continuous Space of Structured Matrices
195. NeurIPS 2024SOI: Scaling Down Computational Complexity by Estimating Partial States of the Model
196. NeurIPS 2024Sparse maximal update parameterization: A holistic approach to sparse training dynamics
197. NeurIPS 2024Surge Phenomenon in Optimal Learning Rate and Batch Size Scaling
198. NeurIPS 2024The Feature Speed Formula: a flexible approach to scale hyper-parameters of deep neural networks
199. NeurIPS 2024Training Compute-Optimal Protein Language Models
200. NeurIPS 2024Understanding Scaling Laws with Statistical and Approximation Theory for Transformer Neural Networks on Intrinsically Low-dimensional Data

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.