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 56 篇，第 1 / 2 页

1. ICML 2025A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control
2. ICML 2025A Hitchhiker's Guide to Scaling Law Estimation
3. ICML 2025A Simple Model of Inference Scaling Laws
4. ICML 2025Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks
5. ICML 2025Contextures: Representations from Contexts
6. ICML 2025DataDecide: How to Predict Best Pretraining Data with Small Experiments
7. ICML 2025Determinant Estimation under Memory Constraints and Neural Scaling Laws
8. ICML 2025Distillation Scaling Laws
9. ICML 2025Does Data Scaling Lead to Visual Compositional Generalization?
10. ICML 2025Dynamic Sparse Training of Diagonally Sparse Networks
11. ICML 2025Equivariant Polynomial Functional Networks
12. ICML 2025Haste Makes Waste: A Simple Approach for Scaling Graph Neural Networks
13. ICML 2025How Do Large Language Monkeys Get Their Power (Laws)?
14. ICML 2025How Much Can We Forget about Data Contamination?
15. ICML 2025Improving Diversity in Language Models: When Temperature Fails, Change the Loss
16. ICML 2025Improving the Scaling Laws of Synthetic Data with Deliberate Practice
17. ICML 2025Joint MoE Scaling Laws: Mixture of Experts Can Be Memory Efficient
18. ICML 2025Learning curves theory for hierarchically compositional data with power-law distributed features
19. ICML 2025Learning to Keep a Promise: Scaling Language Model Decoding Parallelism with Learned Asynchronous Decoding
20. ICML 2025LensLLM: Unveiling Fine-Tuning Dynamics for LLM Selection
21. ICML 2025LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws
22. ICML 2025Memory Layers at Scale
23. ICML 2025Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws
24. ICML 2025Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning
25. ICML 2025No Free Lunch from Random Feature Ensembles: Scaling Laws and Near-Optimality Conditions
26. ICML 2025On Temperature Scaling and Conformal Prediction of Deep Classifiers
27. ICML 2025Over-Tokenized Transformer: Vocabulary is Generally Worth Scaling
28. ICML 2025OWLS: Scaling Laws for Multilingual Speech Recognition and Translation Models
29. ICML 2025Parameters vs FLOPs: Scaling Laws for Optimal Sparsity for Mixture-of-Experts Language Models
30. ICML 2025Parametric Scaling Law of Tuning Bias in Conformal Prediction
31. ICML 2025Penalizing Infeasible Actions and Reward Scaling in Reinforcement Learning with Offline Data
32. ICML 2025Position: AI Scaling: From Up to Down and Out
33. ICML 2025Position: Enough of Scaling LLMs! Lets Focus on Downscaling
34. ICML 2025Position: Scaling LLM Agents Requires Asymptotic Analysis with LLM Primitives
35. ICML 2025Residual Matrix Transformers: Scaling the Size of the Residual Stream
36. ICML 2025Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks
37. ICML 2025Scaling Inference-Efficient Language Models
38. ICML 2025Scaling Large Motion Models with Million-Level Human Motions
39. ICML 2025Scaling Laws for Differentially Private Language Models
40. ICML 2025Scaling Laws for Floating–Point Quantization Training
41. ICML 2025Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection
42. ICML 2025Scaling Laws for Pre-training Agents and World Models
43. ICML 2025Scaling Laws for Task-Optimized Models of the Primate Visual Ventral Stream
44. ICML 2025Scaling Laws for Upcycling Mixture-of-Experts Language Models
45. ICML 2025Scaling Laws in Patchification: An Image Is Worth 50,176 Tokens And More
46. ICML 2025Scaling Probabilistic Circuits via Monarch Matrices
47. ICML 2025Scaling Sparse Feature Circuits For Studying In-Context Learning
48. ICML 2025Scaling Trends in Language Model Robustness
49. ICML 2025Scaling Value Iteration Networks to 5000 Layers for Extreme Long-Term Planning
50. ICML 2025TabFlex: Scaling Tabular Learning to Millions with Linear Attention

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。