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 38 篇，第 1 / 1 页

1. NeurIPS 2024$\texttt{Model-GLUE}$: Democratized LLM Scaling for A Large Model Zoo in the Wild
2. NeurIPS 20244+3 Phases of Compute-Optimal Neural Scaling Laws
3. NeurIPS 2024A Combinatorial Algorithm for the Semi-Discrete Optimal Transport Problem
4. NeurIPS 2024A Fast Convoluted Story: Scaling Probabilistic Inference for Integer Arithmetics
5. NeurIPS 2024An exactly solvable model for emergence and scaling laws in the multitask sparse parity problem
6. NeurIPS 2024ANAH-v2: Scaling Analytical Hallucination Annotation of Large Language Models
7. NeurIPS 2024Are More LLM Calls All You Need? Towards the Scaling Properties of Compound AI Systems
8. NeurIPS 2024Bigger, Regularized, Optimistic: scaling for compute and sample efficient continuous control
9. NeurIPS 2024Consent in Crisis: The Rapid Decline of the AI Data Commons
10. NeurIPS 2024D-CPT Law: Domain-specific Continual Pre-Training Scaling Law for Large Language Models
11. NeurIPS 2024Dimension-free deterministic equivalents and scaling laws for random feature regression
12. NeurIPS 2024fMRI predictors based on language models of increasing complexity recover brain left lateralization
13. NeurIPS 2024MAmmoTH2: Scaling Instructions from the Web
14. NeurIPS 2024MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens
15. NeurIPS 2024Model Collapse Demystified: The Case of Regression
16. NeurIPS 2024Neuc-MDS: Non-Euclidean Multidimensional Scaling Through Bilinear Forms
17. NeurIPS 2024On the Scalability of GNNs for Molecular Graphs
18. NeurIPS 2024Post-Hoc Reversal: Are We Selecting Models Prematurely?
19. NeurIPS 2024Repurposing Language Models into Embedding Models: Finding the Compute-Optimal Recipe
20. NeurIPS 2024Resolving Discrepancies in Compute-Optimal Scaling of Language Models
21. NeurIPS 2024Scaling Continuous Latent Variable Models as Probabilistic Integral Circuits
22. NeurIPS 2024Scaling Law for Time Series Forecasting
23. NeurIPS 2024Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations
24. NeurIPS 2024Scaling laws for learning with real and surrogate data
25. NeurIPS 2024Scaling Laws in Linear Regression: Compute, Parameters, and Data
26. NeurIPS 2024Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies
27. NeurIPS 2024Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained Transformers
28. NeurIPS 2024Scaling Retrieval-Based Language Models with a Trillion-Token Datastore
29. NeurIPS 2024Scaling the Codebook Size of VQ-GAN to 100,000 with a Utilization Rate of 99%
30. NeurIPS 2024Scaling transformer neural networks for skillful and reliable medium-range weather forecasting
31. NeurIPS 2024Scaling White-Box Transformers for Vision
32. NeurIPS 2024Searching for Efficient Linear Layers over a Continuous Space of Structured Matrices
33. NeurIPS 2024SOI: Scaling Down Computational Complexity by Estimating Partial States of the Model
34. NeurIPS 2024Sparse maximal update parameterization: A holistic approach to sparse training dynamics
35. NeurIPS 2024Surge Phenomenon in Optimal Learning Rate and Batch Size Scaling
36. NeurIPS 2024The Feature Speed Formula: a flexible approach to scale hyper-parameters of deep neural networks
37. NeurIPS 2024Training Compute-Optimal Protein Language Models
38. NeurIPS 2024Understanding Scaling Laws with Statistical and Approximation Theory for Transformer Neural Networks on Intrinsically Low-dimensional Data

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。