缩放定律论文趋势 2023–2026

主要结论

• 缩放定律论文从 2023 年的 60 篇增长到 2026 年的 248 篇，三年间扩大到约 4.1 倍。
• NeurIPS 是发缩放定律论文最多的会议，2023–2026 年共 221 篇，占四个会议总量的 37%。
• 交叉方向里涨得最快的是时间序列：同时属于两个主题的论文从 2023 年的 1 篇增至 2026 年的 24 篇。
• 在「优化与缩放」的 5 个主题里，它的 2026 年论文数排第 5；同组增长最快的低秩适配是 3.0 倍，缩放定律是 4.1 倍。

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

• 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

全部缩放定律论文

共 60 篇，第 1 / 2 页

1. CVPR 2023ConvNeXt V2: Co-Designing and Scaling ConvNets With Masked Autoencoders
2. CVPR 2023Galactic: Scaling End-to-End Reinforcement Learning for Rearrangement at 100k Steps-per-Second
3. CVPR 2023LargeKernel3D: Scaling Up Kernels in 3D Sparse CNNs
4. CVPR 2023On Data Scaling in Masked Image Modeling
5. CVPR 2023Reproducible Scaling Laws for Contrastive Language-Image Learning
6. CVPR 2023ResFormer: Scaling ViTs With Multi-Resolution Training
7. CVPR 2023Scaling Language-Image Pre-Training via Masking
8. CVPR 2023VideoMAE V2: Scaling Video Masked Autoencoders With Dual Masking
9. ICLR 2023Block and Subword-Scaling Floating-Point (BSFP) : An Efficient Non-Uniform Quantization For Low Precision Inference
10. ICLR 2023Broken Neural Scaling Laws
11. ICLR 2023Distributionally Robust Post-hoc Classifiers under Prior Shifts
12. ICLR 2023How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization
13. ICLR 2023Massively Scaling Heteroscedastic Classifiers
14. ICLR 2023More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity
15. ICLR 2023Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks
16. ICLR 2023Reparameterization through Spatial Gradient Scaling
17. ICLR 2023Scaling Forward Gradient With Local Losses
18. ICLR 2023Scaling Laws For Deep Learning Based Image Reconstruction
19. ICLR 2023Scaling up and Stabilizing Differentiable Planning with Implicit Differentiation
20. ICLR 2023Scaling Up Probabilistic Circuits by Latent Variable Distillation
21. ICLR 2023The Onset of Variance-Limited Behavior for Networks in the Lazy and Rich Regimes
22. ICLR 2023Unsupervised Manifold Alignment with Joint Multidimensional Scaling
23. ICML 2023Beyond the Universal Law of Robustness: Sharper Laws for Random Features and Neural Tangent Kernels
24. ICML 2023Data Efficient Neural Scaling Law via Model Reusing
25. ICML 2023Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning
26. ICML 2023Hidden Symmetries of ReLU Networks
27. ICML 2023Learning Physical Models that Can Respect Conservation Laws
28. ICML 2023Long Horizon Temperature Scaling
29. ICML 2023On the Convergence Rate of Gaussianization with Random Rotations
30. ICML 2023Online Platt Scaling with Calibeating
31. ICML 2023Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling
32. ICML 2023Scaling Laws for Generative Mixed-Modal Language Models
33. ICML 2023Scaling Laws for Multilingual Neural Machine Translation
34. ICML 2023Scaling Laws for Reward Model Overoptimization
35. ICML 2023Scaling of Class-wise Training Losses for Post-hoc Calibration
36. ICML 2023Scaling Spherical CNNs
37. ICML 2023Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory
38. ICML 2023TAN Without a Burn: Scaling Laws of DP-SGD
39. ICML 2023The case for 4-bit precision: k-bit Inference Scaling Laws
40. NeurIPS 2023A Fast and Accurate Estimator for Large Scale Linear Model via Data Averaging
41. NeurIPS 2023Abide by the law and follow the flow: conservation laws for gradient flows
42. NeurIPS 2023DynaDojo: An Extensible Benchmarking Platform for Scalable Dynamical System Identification
43. NeurIPS 2023Energy-Efficient Scheduling with Predictions
44. NeurIPS 2023Focused Transformer: Contrastive Training for Context Scaling
45. NeurIPS 2023Getting ViT in Shape: Scaling Laws for Compute-Optimal Model Design
46. NeurIPS 2023Hierarchical clustering with dot products recovers hidden tree structure
47. NeurIPS 2023Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition
48. NeurIPS 2023Neural Data Transformer 2: Multi-context Pretraining for Neural Spiking Activity
49. NeurIPS 2023Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sources
50. NeurIPS 2023R-divergence for Estimating Model-oriented Distribution Discrepancy

常见问题

2023–2026 年四大会议一共有多少篇缩放定律论文？

共 605 篇，按年份分别是 2023 年 60 篇、2024 年 96 篇、2025 年 201 篇、2026 年 248 篇。

NeurIPS 2026 有多少篇缩放定律论文？

NeurIPS 2026 有 82 篇缩放定律论文，上一届是 80 篇。

ICLR 2026 有多少篇缩放定律论文？

ICLR 2026 有 73 篇缩放定律论文，上一届是 48 篇。

ICML 2026 有多少篇缩放定律论文？

ICML 2026 有 78 篇缩放定律论文，上一届是 56 篇。

CVPR 2026 有多少篇缩放定律论文？

CVPR 2026 有 15 篇缩放定律论文，上一届是 17 篇。

哪个会议的缩放定律论文最多？

NeurIPS，2023–2026 年共 221 篇。