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 184 篇，第 3 / 4 页

101. ICML 2025Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws
102. ICML 2025Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning
103. ICML 2025No Free Lunch from Random Feature Ensembles: Scaling Laws and Near-Optimality Conditions
104. ICML 2025On Temperature Scaling and Conformal Prediction of Deep Classifiers
105. ICML 2025Over-Tokenized Transformer: Vocabulary is Generally Worth Scaling
106. ICML 2025OWLS: Scaling Laws for Multilingual Speech Recognition and Translation Models
107. ICML 2025Parameters vs FLOPs: Scaling Laws for Optimal Sparsity for Mixture-of-Experts Language Models
108. ICML 2025Parametric Scaling Law of Tuning Bias in Conformal Prediction
109. ICML 2025Penalizing Infeasible Actions and Reward Scaling in Reinforcement Learning with Offline Data
110. ICML 2025Position: AI Scaling: From Up to Down and Out
111. ICML 2025Position: Enough of Scaling LLMs! Lets Focus on Downscaling
112. ICML 2025Position: Scaling LLM Agents Requires Asymptotic Analysis with LLM Primitives
113. ICML 2025Residual Matrix Transformers: Scaling the Size of the Residual Stream
114. ICML 2025Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks
115. ICML 2025Scaling Inference-Efficient Language Models
116. ICML 2025Scaling Large Motion Models with Million-Level Human Motions
117. ICML 2025Scaling Laws for Differentially Private Language Models
118. ICML 2025Scaling Laws for Floating–Point Quantization Training
119. ICML 2025Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection
120. ICML 2025Scaling Laws for Pre-training Agents and World Models
121. ICML 2025Scaling Laws for Task-Optimized Models of the Primate Visual Ventral Stream
122. ICML 2025Scaling Laws for Upcycling Mixture-of-Experts Language Models
123. ICML 2025Scaling Laws in Patchification: An Image Is Worth 50,176 Tokens And More
124. ICML 2025Scaling Probabilistic Circuits via Monarch Matrices
125. ICML 2025Scaling Sparse Feature Circuits For Studying In-Context Learning
126. ICML 2025Scaling Trends in Language Model Robustness
127. ICML 2025Scaling Value Iteration Networks to 5000 Layers for Extreme Long-Term Planning
128. ICML 2025TabFlex: Scaling Tabular Learning to Millions with Linear Attention
129. ICML 2025The Importance of Being Lazy: Scaling Limits of Continual Learning
130. ICML 2025The Underlying Universal Statistical Structure of Natural Datasets
131. ICML 2025Transformative or Conservative? Conservation laws for ResNets and Transformers
132. ICML 2025Value-Based Deep RL Scales Predictably
133. ICML 2025Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive?
134. ICML 2025µnit Scaling: Simple and Scalable FP8 LLM Training
135. ICML 2024A Dynamical Model of Neural Scaling Laws
136. ICML 2024A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization
137. ICML 2024A Tale of Tails: Model Collapse as a Change of Scaling Laws
138. ICML 2024Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies
139. ICML 2024Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws
140. ICML 2024Data Engineering for Scaling Language Models to 128K Context
141. ICML 2024Differentiable Model Scaling using Differentiable Topk
142. ICML 2024Explorations of Self-Repair in Language Models
143. ICML 2024Feature Reuse and Scaling: Understanding Transfer Learning with Protein Language Models
144. ICML 2024Harnessing the Power of Neural Operators with Automatically Encoded Conservation Laws
145. ICML 2024IBD-PSC: Input-level Backdoor Detection via Parameter-oriented Scaling Consistency
146. ICML 2024Keep the Momentum: Conservation Laws beyond Euclidean Gradient Flows
147. ICML 2024Mechanistic Design and Scaling of Hybrid Architectures
148. ICML 2024Mixtures of Experts Unlock Parameter Scaling for Deep RL
149. ICML 2024Navigating Scaling Laws: Compute Optimality in Adaptive Model Training
150. ICML 2024Offline Actor-Critic Reinforcement Learning Scales to Large Models

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。