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 605 篇，第 2 / 13 页

51. ICLR 2026Revisiting the Scaling Properties of Downstream Metrics in Large Language Model Training
52. ICLR 2026ROC-n-reroll: How verifier imperfection affects test-time scaling
53. ICLR 2026Sample Complexity and Representation Ability of Test-time Scaling Paradigms
54. ICLR 2026Scaling Agent Learning via Experience Synthesis
55. ICLR 2026Scaling Agents via Continual Pre-training
56. ICLR 2026Scaling Attention via Feature Sparsity
57. ICLR 2026Scaling Behavior of Discrete Diffusion Language Models
58. ICLR 2026Scaling Direct Feedback Learning with Theoretical Guarantees
59. ICLR 2026Scaling Generalist Data-Analytic Agents
60. ICLR 2026Scaling Goal-conditioned Reinforcement Learning with Multistep Quasimetric Distances
61. ICLR 2026Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database
62. ICLR 2026Scaling Laws and Spectra of Shallow Neural Networks in the Feature Learning Regime
63. ICLR 2026Scaling Laws and Symmetry, Evidence from Neural Force Fields
64. ICLR 2026Scaling Laws for Diffusion Transformers
65. ICLR 2026Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs
66. ICLR 2026Scaling Laws of SignSGD in Linear Regression: When Does It Outperform SGD?
67. ICLR 2026Scaling Laws Revisited: Modeling the Role of Data Quality in Language Model Pretraining
68. ICLR 2026Scaling Linear Attention with Sparse State Expansion
69. ICLR 2026Scaling Reasoning Hop Exposes Weaknesses: Demystifying and Improving Hop Generalization in Large Language Models
70. ICLR 2026Scaling Sequence-to-Sequence Generative Neural Rendering
71. ICLR 2026Scaling with Collapse: Efficient and Predictable Training of LLM Families
72. ICLR 2026STEM: SCALING TRANSFORMERS WITH EMBEDDING MODULES
73. ICLR 2026The Hot Mess of AI: How Does Misalignment Scale With Model Intelligence and Task Complexity?
74. ICLR 2026The Limits of Inference Scaling Through Resampling
75. ICLR 2026The Serial Scaling Hypothesis
76. ICLR 2026Theory of Scaling Laws for In-Context Regression: Depth, Width, Context and Time
77. ICLR 2026Toward Principled Flexible Scaling for Self-Gated Neural Activation
78. ICLR 2026Towards Greater Leverage: Scaling Laws for Efficient Mixture-of-Experts Language Models
79. ICLR 2026TrimR: Verifier-based Training-Free Thinking Trimming for Efficient Test-Time Scaling
80. ICLR 2026UltraMemV2: Memory Networks Scaling to 120B Parameters with Superior Long-Context Learning
81. ICLR 2026Understanding the Mechanisms of Fast Hyperparameter Transfer
82. ICLR 2026Unveiling Downstream Performance Scaling of LLMs: A Clustering-Based Perspective
83. ICLR 2026UrbanVerse: Scaling Urban Simulation by Watching City-Tour Videos
84. ICLR 2026WATS: Wavelet-Aware Temperature Scaling for Reliable Graph Neural Networks
85. ICLR 2026Webscale-RL: Automated Data Pipeline for Scaling RL Data to Pretraining Levels
86. ICLR 2026What Scales in Cross-Entropy Scaling Law?
87. ICLR 2026Why Less is More (Sometimes): A Theory of Data Curation
88. ICLR 2026xLSTM Scaling Laws: Competitive Performance with Linear Time-Complexity
89. ICML 2026$L^3$: Large Lookup Layers
90. ICML 2026A Capacity-Based Rationale for Multi-Head Attention
91. ICML 2026Active Budget Allocation for Efficient Scaling Law Estimation via Surrogate-Guided Pruning
92. ICML 2026Balancing Learning Rates Across Layers: Exact Two-Step Dynamics and Optimal Scaling in Linear Neural Networks
93. ICML 2026Branch Scaling Manifests as Implicit Architectural Regularization for Improving Generalization in Overparameterized ResNets
94. ICML 2026cMoLLM at Scale: Horizontal Scaling Laws for Convolutionally-Gated Mixture-of-LLMs
95. ICML 2026Conservation Laws for Modern Neural Architectures
96. ICML 2026Curriculum-Guided Layer Scaling for Language Model Pretraining
97. ICML 2026Decouple Searching from Training: Scaling Data Mixing via Model Merging for Large Language Model Pre-training
98. ICML 2026Demystifying LLM-as-a-Judge: Analytically Tractable Model for Inference-Time Scaling
99. ICML 2026Deriving Neural Scaling Laws from the Statistics of Natural Language
100. ICML 2026Discovering Scaling Exponents with Physics-Informed Müntz-Szász Networks

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。