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 221 篇，第 3 / 5 页

101. NeurIPS 2025Emergence and scaling laws in SGD learning of shallow neural networks
102. NeurIPS 2025Exploring Data Scaling Trends and Effects in Reinforcement Learning from Human Feedback
103. NeurIPS 2025Functional Scaling Laws in Kernel Regression: Loss Dynamics and Learning Rate Schedules
104. NeurIPS 2025Gemstones: A Model Suite for Multi-Faceted Scaling Laws
105. NeurIPS 2025GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large Language Models to 8K Resolution
106. NeurIPS 2025GPAS: Accelerating Convergence of LLM Pretraining via Gradient-Preserving Activation Scaling
107. NeurIPS 2025HiFC: High-efficiency Flash-based KV Cache Swapping for Scaling LLM Inference
108. NeurIPS 2025How to Scale Second-Order Optimization
109. NeurIPS 2025Improved Scaling Laws in Linear Regression via Data Reuse
110. NeurIPS 2025Inference-Time Hyper-Scaling with KV Cache Compression
111. NeurIPS 2025Infinite-Width Limit of a Single Attention Layer: Analysis via Tensor Programs
112. NeurIPS 2025Is Limited Participant Diversity Impeding EEG-based Machine Learning?
113. NeurIPS 2025Kinetics: Rethinking Test-Time Scaling Law
114. NeurIPS 2025L$^2$M: Mutual Information Scaling Law for Long-Context Language Modeling
115. NeurIPS 2025LCDB 1.1: A Database Illustrating Learning Curves Are More Ill-Behaved Than Previously Thought
116. NeurIPS 2025Learning in Compact Spaces with Approximately Normalized Transformer
117. NeurIPS 2025Learning quadratic neural networks in high dimensions: SGD dynamics and scaling laws
118. NeurIPS 2025Meta CLIP 2: A Worldwide Scaling Recipe
119. NeurIPS 2025Mind the GAP! The Challenges of Scale in Pixel-based Deep Reinforcement Learning
120. NeurIPS 2025NaViL: Rethinking Scaling Properties of Native Multimodal Large Language Models under Data Constraints
121. NeurIPS 2025NAVIX: Scaling MiniGrid Environments with JAX
122. NeurIPS 2025On the Entropy Calibration of Language Models
123. NeurIPS 2025On the Surprising Effectiveness of Large Learning Rates under Standard Width Scaling
124. NeurIPS 2025Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model
125. NeurIPS 2025Optimal and Provable Calibration in High-Dimensional Binary Classification: Angular Calibration and Platt Scaling
126. NeurIPS 2025P-Law: Predicting Quantitative Scaling Law with Entropy Guidance in Large Recommendation Models
127. NeurIPS 2025Parallel Scaling Law for Language Models
128. NeurIPS 2025ParetoQ: Improving Scaling Laws in Extremely Low-bit LLM Quantization
129. NeurIPS 2025Power Lines: Scaling laws for weight decay and batch size in LLM pre-training
130. NeurIPS 2025Predictable Scale (Part II) --- Farseer: A Refined Scaling Law in LLMs
131. NeurIPS 2025Provable Scaling Laws for the Test-Time Compute of Large Language Models
132. NeurIPS 2025Recursive Inference Scaling: A Winning Path to Scalable Inference in Language and Multimodal Systems
133. NeurIPS 2025Remasking Discrete Diffusion Models with Inference-Time Scaling
134. NeurIPS 2025RigAnyFace: Scaling Neural Facial Mesh Auto-Rigging with Unlabeled Data
135. NeurIPS 2025RoMA: Scaling up Mamba-based Foundation Models for Remote Sensing
136. NeurIPS 2025Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection
137. NeurIPS 2025Scaling and context steer LLMs along the same computational path as the human brain
138. NeurIPS 2025Scaling can lead to compositional generalization
139. NeurIPS 2025Scaling Embedding Layers in Language Models
140. NeurIPS 2025Scaling Epidemic Inference on Contact Networks: Theory and Algorithms
141. NeurIPS 2025Scaling Image Geo-Localization to Continent Level
142. NeurIPS 2025Scaling Language-centric Omnimodal Representation Learning
143. NeurIPS 2025Scaling Law with Learning Rate Annealing
144. NeurIPS 2025Scaling Laws for Gradient Descent and Sign Descent for Linear Bigram Models under Zipf’s Law
145. NeurIPS 2025Scaling Laws for Optimal Data Mixtures
146. NeurIPS 2025Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets
147. NeurIPS 2025Scaling Laws For Scalable Oversight
148. NeurIPS 2025Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization
149. NeurIPS 2025Scaling Offline RL via Efficient and Expressive Shortcut Models
150. NeurIPS 2025Scaling Speculative Decoding with Lookahead Reasoning

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。