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
184 papers, page 3 of 4
- ICML 2025Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws
- ICML 2025Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning
- ICML 2025No Free Lunch from Random Feature Ensembles: Scaling Laws and Near-Optimality Conditions
- ICML 2025On Temperature Scaling and Conformal Prediction of Deep Classifiers
- ICML 2025Over-Tokenized Transformer: Vocabulary is Generally Worth Scaling
- ICML 2025OWLS: Scaling Laws for Multilingual Speech Recognition and Translation Models
- ICML 2025Parameters vs FLOPs: Scaling Laws for Optimal Sparsity for Mixture-of-Experts Language Models
- ICML 2025Parametric Scaling Law of Tuning Bias in Conformal Prediction
- ICML 2025Penalizing Infeasible Actions and Reward Scaling in Reinforcement Learning with Offline Data
- ICML 2025Position: AI Scaling: From Up to Down and Out
- ICML 2025Position: Enough of Scaling LLMs! Lets Focus on Downscaling
- ICML 2025Position: Scaling LLM Agents Requires Asymptotic Analysis with LLM Primitives
- ICML 2025Residual Matrix Transformers: Scaling the Size of the Residual Stream
- ICML 2025Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks
- ICML 2025Scaling Inference-Efficient Language Models
- ICML 2025Scaling Large Motion Models with Million-Level Human Motions
- ICML 2025Scaling Laws for Differentially Private Language Models
- ICML 2025Scaling Laws for Floating–Point Quantization Training
- ICML 2025Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection
- ICML 2025Scaling Laws for Pre-training Agents and World Models
- ICML 2025Scaling Laws for Task-Optimized Models of the Primate Visual Ventral Stream
- ICML 2025Scaling Laws for Upcycling Mixture-of-Experts Language Models
- ICML 2025Scaling Laws in Patchification: An Image Is Worth 50,176 Tokens And More
- ICML 2025Scaling Probabilistic Circuits via Monarch Matrices
- ICML 2025Scaling Sparse Feature Circuits For Studying In-Context Learning
- ICML 2025Scaling Trends in Language Model Robustness
- ICML 2025Scaling Value Iteration Networks to 5000 Layers for Extreme Long-Term Planning
- ICML 2025TabFlex: Scaling Tabular Learning to Millions with Linear Attention
- ICML 2025The Importance of Being Lazy: Scaling Limits of Continual Learning
- ICML 2025The Underlying Universal Statistical Structure of Natural Datasets
- ICML 2025Transformative or Conservative? Conservation laws for ResNets and Transformers
- ICML 2025Value-Based Deep RL Scales Predictably
- ICML 2025Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive?
- ICML 2025µnit Scaling: Simple and Scalable FP8 LLM Training
- ICML 2024A Dynamical Model of Neural Scaling Laws
- ICML 2024A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization
- ICML 2024A Tale of Tails: Model Collapse as a Change of Scaling Laws
- ICML 2024Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies
- ICML 2024Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws
- ICML 2024Data Engineering for Scaling Language Models to 128K Context
- ICML 2024Differentiable Model Scaling using Differentiable Topk
- ICML 2024Explorations of Self-Repair in Language Models
- ICML 2024Feature Reuse and Scaling: Understanding Transfer Learning with Protein Language Models
- ICML 2024Harnessing the Power of Neural Operators with Automatically Encoded Conservation Laws
- ICML 2024IBD-PSC: Input-level Backdoor Detection via Parameter-oriented Scaling Consistency
- ICML 2024Keep the Momentum: Conservation Laws beyond Euclidean Gradient Flows
- ICML 2024Mechanistic Design and Scaling of Hybrid Architectures
- ICML 2024Mixtures of Experts Unlock Parameter Scaling for Deep RL
- ICML 2024Navigating Scaling Laws: Compute Optimality in Adaptive Model Training
- ICML 2024Offline Actor-Critic Reinforcement Learning Scales to Large Models
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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.