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

248 papers, page 5 of 5

201. NeurIPS 2026Normalized Architectures are Natively 4-Bit
202. NeurIPS 2026OmniDex: Scaling Dexterous Hand Grasping to Diverse Cluttered Scenes
203. NeurIPS 2026On Data Engineering for Scaling LLM Terminal Capabilities
204. NeurIPS 2026On Neural Scaling Laws for Weather Emulation through Continual Training
205. NeurIPS 2026On the Nonlinearity of Learning Rate Scaling for LLM Training
206. NeurIPS 2026On the Recall Scaling Laws in Mamba: A Theoretical and Mechanistic Study via Hashing
207. NeurIPS 2026Optimal scaling laws in learning hierarchical multi-index models
208. NeurIPS 2026Overcoming Kernel Redundancy for Scaling Logic Gate Networks
209. NeurIPS 2026Predicting and improving test-time scaling laws via reward tail-guided search
210. NeurIPS 2026Problem-Dependent Dynamic Regret over a Predictor Class with One-Gradient Feedback
211. NeurIPS 2026Provable Test-Time Scaling for Beam Search in LLM Reasoning
212. NeurIPS 2026Random Attention Pattern Learning Enables Emergent Capabilities
213. NeurIPS 2026RaZeR: Pushing the Limits of NVFP4 Quantization with Redundant Zero Remapping
214. NeurIPS 2026Reasoning Warm-up: Scaling Label-free RL via Verifiable Surrogate Rewards
215. NeurIPS 2026Reference-Guided Training: Adaptive Gradient Scaling via Per-Sample Loss Comparisons
216. NeurIPS 2026Reinforce Adjoint Matching: Scaling RL Post-Training of Diffusion and Flow Models
217. NeurIPS 2026Rethinking Language Model Scaling under Transferable Hypersphere Optimization
218. NeurIPS 2026Scale Where It Matters: Training-Free Localized Scaling for Diffusion Models
219. NeurIPS 2026Scaling Arbitrary Architectures and Optimizers with Automatic Parameterization
220. NeurIPS 2026Scaling Genomic Language Modeling with Unified Corpus and Evaluation in Bacteria
221. NeurIPS 2026Scaling Laws and Tradeoffs in Recurrent Networks of Expressive Neurons
222. NeurIPS 2026Scaling Laws for SAE Training Data
223. NeurIPS 2026Scaling Linear Mode Connectivity and Merging to Billion Parameter Pretrained Transformers
224. NeurIPS 2026Scaling Neural Motor Decoding via Decoupled Behavioral Pretraining
225. NeurIPS 2026Scaling Optimization-Oriented Hypernetworks for Implicit Neural Representations
226. NeurIPS 2026Scaling Storm-Resolving Atmospheric AI Simulation to the Entire Planet
227. NeurIPS 2026SDS-LoRA: Overcoming Anisotropic Gradient Scaling in Low-Rank Adaptation
228. NeurIPS 2026Selected-Tail Reliability in Verifier-Guided Best-of-$N$ Inference
229. NeurIPS 2026SETA: Scaling Environments for Terminal Agents
230. NeurIPS 2026Sharp Capacity Scaling of Spectral Optimizers in Learning Associative Memory
231. NeurIPS 2026Sharp feature-learning transitions and Bayes-optimal neural scaling laws in extensive-width networks
232. NeurIPS 2026Sharpness, Stability, and Step-Size Scaling in Deep Polynomial Networks
233. NeurIPS 2026SWE-Crafter: Scaling Executable Multilingual Software Engineering Data with Meta-Skill Agents
234. NeurIPS 2026Systematic Scaling Analysis of Jailbreak Attacks in Large Language Models
235. NeurIPS 2026Tail Competition Explains Scaling in Best-of-N Sampling for Verifiable Problems
236. NeurIPS 2026Target-Budget Best-Function Identification for Scaling-Law-Guided Model Selection
237. NeurIPS 2026Test-time Scaling of Diffusions with Flow Maps
238. NeurIPS 2026Test-Time Scaling with Diffusion Language Models via Reward-Guided Stitching
239. NeurIPS 2026The Geometric Wall: Manifold Structure Predicts Layerwise Sparse Autoencoder Scaling Laws
240. NeurIPS 2026The Hidden Power of Scaling Factor in LoRA Optimization
241. NeurIPS 2026The Minimax Rate of Second-Order Calibration
242. NeurIPS 2026The Scaling Laws of Skills in LLM Agent Systems
243. NeurIPS 2026Theory of Optimal Learning Rate Schedules and Scaling Laws for a Random Feature Model
244. NeurIPS 2026Tokens-per-Parameter Coverage Is Critical for Robust LLM Scaling Law Extrapolation
245. NeurIPS 2026Training Optimal Large Diffusion Language Models
246. NeurIPS 2026What should post-training optimize? A test-time scaling law perspective
247. NeurIPS 2026When Less is More: The LLM Scaling Paradox in Context Compression
248. NeurIPS 2026When Reasoning Meets Its Laws

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.