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 80 篇，第 2 / 2 页

51. NeurIPS 2025Remasking Discrete Diffusion Models with Inference-Time Scaling
52. NeurIPS 2025RigAnyFace: Scaling Neural Facial Mesh Auto-Rigging with Unlabeled Data
53. NeurIPS 2025RoMA: Scaling up Mamba-based Foundation Models for Remote Sensing
54. NeurIPS 2025Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection
55. NeurIPS 2025Scaling and context steer LLMs along the same computational path as the human brain
56. NeurIPS 2025Scaling can lead to compositional generalization
57. NeurIPS 2025Scaling Embedding Layers in Language Models
58. NeurIPS 2025Scaling Epidemic Inference on Contact Networks: Theory and Algorithms
59. NeurIPS 2025Scaling Image Geo-Localization to Continent Level
60. NeurIPS 2025Scaling Language-centric Omnimodal Representation Learning
61. NeurIPS 2025Scaling Law with Learning Rate Annealing
62. NeurIPS 2025Scaling Laws for Gradient Descent and Sign Descent for Linear Bigram Models under Zipf’s Law
63. NeurIPS 2025Scaling Laws for Optimal Data Mixtures
64. NeurIPS 2025Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets
65. NeurIPS 2025Scaling Laws For Scalable Oversight
66. NeurIPS 2025Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization
67. NeurIPS 2025Scaling Offline RL via Efficient and Expressive Shortcut Models
68. NeurIPS 2025Scaling Speculative Decoding with Lookahead Reasoning
69. NeurIPS 2025Scaling Unlocks Broader Generation and Deeper Functional Understanding of Proteins
70. NeurIPS 2025Scaling Up Active Testing to Large Language Models
71. NeurIPS 2025Sloth: scaling laws for LLM skills to predict multi-benchmark performance across families
72. NeurIPS 2025Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism
73. NeurIPS 2025Superposition Yields Robust Neural Scaling
74. NeurIPS 2025SWE-smith: Scaling Data for Software Engineering Agents
75. NeurIPS 2025TabDPT: Scaling Tabular Foundation Models on Real Data
76. NeurIPS 2025The Curse of Depth in Large Language Models
77. NeurIPS 2025Understanding LLM Behaviors via Compression: Data Generation, Knowledge Acquisition and Scaling Laws
78. NeurIPS 2025Unified Scaling Laws for Compressed Representations
79. NeurIPS 2025When Worse is Better: Navigating the Compression Generation Trade-off In Visual Tokenization
80. NeurIPS 2025Zero-Shot Performance Prediction for Probabilistic Scaling Laws

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。