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 48 篇，第 1 / 1 页

1. ICLR 2025(Mis)Fitting Scaling Laws: A Survey of Scaling Law Fitting Techniques in Deep Learning
2. ICLR 2025A Multi-Power Law for Loss Curve Prediction Across Learning Rate Schedules
3. ICLR 2025A Solvable Attention for Neural Scaling Laws
4. ICLR 2025Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws
5. ICLR 2025Analyzing Neural Scaling Laws in Two-Layer Networks with Power-Law Data Spectra
6. ICLR 2025Beyond Model Collapse: Scaling Up with Synthesized Data Requires Verification
7. ICLR 2025Breaking Neural Network Scaling Laws with Modularity
8. ICLR 2025Compute-Constrained Data Selection
9. ICLR 2025Compute-Optimal LLMs Provably Generalize Better with Scale
10. ICLR 2025Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance
11. ICLR 2025Data Scaling Laws in Imitation Learning for Robotic Manipulation
12. ICLR 2025DCT-CryptoNets: Scaling Private Inference in the Frequency Domain
13. ICLR 2025Fast Training of Sinusoidal Neural Fields via Scaling Initialization
14. ICLR 2025GETS: Ensemble Temperature Scaling for Calibration in Graph Neural Networks
15. ICLR 2025High-dimensional Analysis of Knowledge Distillation: Weak-to-Strong Generalization and Scaling Laws
16. ICLR 2025How Feature Learning Can Improve Neural Scaling Laws
17. ICLR 2025How Much is a Noisy Image Worth? Data Scaling Laws for Ambient Diffusion.
18. ICLR 2025Inference Optimal VLMs Need Fewer Visual Tokens and More Parameters
19. ICLR 2025Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for LLM Problem-Solving
20. ICLR 2025Language models scale reliably with over-training and on downstream tasks
21. ICLR 2025LiNeS: Post-training Layer Scaling Prevents Forgetting and Enhances Model Merging
22. ICLR 2025On Scaling Up 3D Gaussian Splatting Training
23. ICLR 2025OSTQuant: Refining Large Language Model Quantization with Orthogonal and Scaling Transformations for Better Distribution Fitting
24. ICLR 2025Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws
25. ICLR 2025Progressive Token Length Scaling in Transformer Encoders for Efficient Universal Segmentation
26. ICLR 2025Quest: Query-centric Data Synthesis Approach for Long-context Scaling of Large Language Model
27. ICLR 2025Reasoning with Latent Thoughts: On the Power of Looped Transformers
28. ICLR 2025Scaling and evaluating sparse autoencoders
29. ICLR 2025Scaling Autonomous Agents via Automatic Reward Modeling And Planning
30. ICLR 2025Scaling Diffusion Language Models via Adaptation from Autoregressive Models
31. ICLR 2025Scaling FP8 training to trillion-token LLMs
32. ICLR 2025Scaling Laws for Adversarial Attacks on Language Model Activations and Tokens
33. ICLR 2025Scaling Laws for Downstream Task Performance in Machine Translation
34. ICLR 2025Scaling Laws for Precision
35. ICLR 2025Scaling Long Context Training Data by Long-Distance Referrals
36. ICLR 2025Scaling Optimal LR Across Token Horizons
37. ICLR 2025Scaling Stick-Breaking Attention: An Efficient Implementation and In-depth Study
38. ICLR 2025Scaling up Masked Diffusion Models on Text
39. ICLR 2025Scaling Wearable Foundation Models
40. ICLR 2025SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning
41. ICLR 2025Strong Model Collapse
42. ICLR 2025Surprising Effectiveness of pretraining Ternary Language Model at Scale
43. ICLR 2025The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws
44. ICLR 2025TokenFormer: Rethinking Transformer Scaling with Tokenized Model Parameters
45. ICLR 2025Towards Neural Scaling Laws for Time Series Foundation Models
46. ICLR 2025Transformers Struggle to Learn to Search
47. ICLR 2025U-shaped and Inverted-U Scaling behind Emergent Abilities of Large Language Models
48. ICLR 2025Ultra-Sparse Memory Network

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。