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 201 篇，第 2 / 5 页

51. ICLR 2025Scaling Laws for Precision
52. ICLR 2025Scaling Long Context Training Data by Long-Distance Referrals
53. ICLR 2025Scaling Optimal LR Across Token Horizons
54. ICLR 2025Scaling Stick-Breaking Attention: An Efficient Implementation and In-depth Study
55. ICLR 2025Scaling up Masked Diffusion Models on Text
56. ICLR 2025Scaling Wearable Foundation Models
57. ICLR 2025SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning
58. ICLR 2025Strong Model Collapse
59. ICLR 2025Surprising Effectiveness of pretraining Ternary Language Model at Scale
60. ICLR 2025The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws
61. ICLR 2025TokenFormer: Rethinking Transformer Scaling with Tokenized Model Parameters
62. ICLR 2025Towards Neural Scaling Laws for Time Series Foundation Models
63. ICLR 2025Transformers Struggle to Learn to Search
64. ICLR 2025U-shaped and Inverted-U Scaling behind Emergent Abilities of Large Language Models
65. ICLR 2025Ultra-Sparse Memory Network
66. ICML 2025A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control
67. ICML 2025A Hitchhiker's Guide to Scaling Law Estimation
68. ICML 2025A Simple Model of Inference Scaling Laws
69. ICML 2025Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks
70. ICML 2025Contextures: Representations from Contexts
71. ICML 2025DataDecide: How to Predict Best Pretraining Data with Small Experiments
72. ICML 2025Determinant Estimation under Memory Constraints and Neural Scaling Laws
73. ICML 2025Distillation Scaling Laws
74. ICML 2025Does Data Scaling Lead to Visual Compositional Generalization?
75. ICML 2025Dynamic Sparse Training of Diagonally Sparse Networks
76. ICML 2025Equivariant Polynomial Functional Networks
77. ICML 2025Haste Makes Waste: A Simple Approach for Scaling Graph Neural Networks
78. ICML 2025How Do Large Language Monkeys Get Their Power (Laws)?
79. ICML 2025How Much Can We Forget about Data Contamination?
80. ICML 2025Improving Diversity in Language Models: When Temperature Fails, Change the Loss
81. ICML 2025Improving the Scaling Laws of Synthetic Data with Deliberate Practice
82. ICML 2025Joint MoE Scaling Laws: Mixture of Experts Can Be Memory Efficient
83. ICML 2025Learning curves theory for hierarchically compositional data with power-law distributed features
84. ICML 2025Learning to Keep a Promise: Scaling Language Model Decoding Parallelism with Learned Asynchronous Decoding
85. ICML 2025LensLLM: Unveiling Fine-Tuning Dynamics for LLM Selection
86. ICML 2025LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws
87. ICML 2025Memory Layers at Scale
88. ICML 2025Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws
89. ICML 2025Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning
90. ICML 2025No Free Lunch from Random Feature Ensembles: Scaling Laws and Near-Optimality Conditions
91. ICML 2025On Temperature Scaling and Conformal Prediction of Deep Classifiers
92. ICML 2025Over-Tokenized Transformer: Vocabulary is Generally Worth Scaling
93. ICML 2025OWLS: Scaling Laws for Multilingual Speech Recognition and Translation Models
94. ICML 2025Parameters vs FLOPs: Scaling Laws for Optimal Sparsity for Mixture-of-Experts Language Models
95. ICML 2025Parametric Scaling Law of Tuning Bias in Conformal Prediction
96. ICML 2025Penalizing Infeasible Actions and Reward Scaling in Reinforcement Learning with Offline Data
97. ICML 2025Position: AI Scaling: From Up to Down and Out
98. ICML 2025Position: Enough of Scaling LLMs! Lets Focus on Downscaling
99. ICML 2025Position: Scaling LLM Agents Requires Asymptotic Analysis with LLM Primitives
100. ICML 2025Residual Matrix Transformers: Scaling the Size of the Residual Stream

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。