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 33 篇，第 1 / 1 页

1. ICML 2024A Dynamical Model of Neural Scaling Laws
2. ICML 2024A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization
3. ICML 2024A Tale of Tails: Model Collapse as a Change of Scaling Laws
4. ICML 2024Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies
5. ICML 2024Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws
6. ICML 2024Data Engineering for Scaling Language Models to 128K Context
7. ICML 2024Differentiable Model Scaling using Differentiable Topk
8. ICML 2024Explorations of Self-Repair in Language Models
9. ICML 2024Feature Reuse and Scaling: Understanding Transfer Learning with Protein Language Models
10. ICML 2024Harnessing the Power of Neural Operators with Automatically Encoded Conservation Laws
11. ICML 2024IBD-PSC: Input-level Backdoor Detection via Parameter-oriented Scaling Consistency
12. ICML 2024Keep the Momentum: Conservation Laws beyond Euclidean Gradient Flows
13. ICML 2024Mechanistic Design and Scaling of Hybrid Architectures
14. ICML 2024Mixtures of Experts Unlock Parameter Scaling for Deep RL
15. ICML 2024Navigating Scaling Laws: Compute Optimality in Adaptive Model Training
16. ICML 2024Offline Actor-Critic Reinforcement Learning Scales to Large Models
17. ICML 2024On the Embedding Collapse when Scaling up Recommendation Models
18. ICML 2024Position: Will we run out of data? Limits of LLM scaling based on human-generated data
19. ICML 2024Provably Scalable Black-Box Variational Inference with Structured Variational Families
20. ICML 2024Random Scaling and Momentum for Non-smooth Non-convex Optimization
21. ICML 2024RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback
22. ICML 2024Scaling Beyond the GPU Memory Limit for Large Mixture-of-Experts Model Training
23. ICML 2024Scaling Down Deep Learning with MNIST-1D
24. ICML 2024Scaling Exponents Across Parameterizations and Optimizers
25. ICML 2024Scaling Laws for Fine-Grained Mixture of Experts
26. ICML 2024Scaling Laws for the Value of Individual Data Points in Machine Learning
27. ICML 2024Scaling Tractable Probabilistic Circuits: A Systems Perspective
28. ICML 2024Selecting Large Language Model to Fine-tune via Rectified Scaling Law
29. ICML 2024SelMatch: Effectively Scaling Up Dataset Distillation via Selection-Based Initialization and Partial Updates by Trajectory Matching
30. ICML 2024SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Models
31. ICML 2024Testing the Feasibility of Linear Programs with Bandit Feedback
32. ICML 2024Training-Free Long-Context Scaling of Large Language Models
33. ICML 2024Wukong: Towards a Scaling Law for Large-Scale Recommendation

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。