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 73 篇，第 1 / 2 页

1. ICLR 2026$\mathbf{Li_2}$: A Framework on Dynamics of Feature Emergence and Delayed Generalization
2. ICLR 2026A universal compression theory: Lottery ticket hypothesis and superpolynomial scaling laws
3. ICLR 2026Agentic Collaboration as an Information Bottleneck Problem
4. ICLR 2026ATLAS: Adaptive Transfer Scaling Laws for Multilingual Pretraining, Finetuning, and Decoding the Curse of Multilinguality
5. ICLR 2026Best-of-Majority: Minimax-Optimal Strategy for Pass@k Inference Scaling
6. ICLR 2026Can Language Models Discover Scaling Laws?
7. ICLR 2026Capability-Based Scaling Trends for LLM-Based Red-Teaming
8. ICLR 2026Closed-form $\ell_r$ norm scaling with data for overparameterized linear regression and diagonal linear networks under $\ell_p$ bias
9. ICLR 2026Code2Bench: Scaling Source and Rigor for Dynamic Benchmark Construction
10. ICLR 2026Compute-Optimal Quantization-Aware Training
11. ICLR 2026ContextPRM: Leveraging Contextual Coherence for multi-domain Test-Time Scaling
12. ICLR 2026Convex Dominance in Deep Learning I: A Scaling Law of Loss and Learning Rate
13. ICLR 2026CR-Net: Scaling Parameter-Efficient Training with Cross-Layer Low-Rank Structure
14. ICLR 2026Critical attention scaling in long-context transformers
15. ICLR 2026Decomposing Extrapolative Problem Solving: Spatial Transfer and Length Scaling with Map Worlds
16. ICLR 2026Diagnosing and Improving Diffusion Models by Estimating Optimal Loss Value
17. ICLR 2026Distilled Pretraining: A modern lens of Data, In-Context Learning and Test-Time Scaling
18. ICLR 2026Fast Catch-Up, Late Switching: Optimal Batch Size Scheduling via Functional Scaling Laws
19. ICLR 2026Fast Escape, Slow Convergence: Learning Dynamics of Phase Retrieval under Power-Law Data
20. ICLR 2026From Curiosity to Caution: Mitigating Reward Hacking for Best-of-$N$ with Pessimism
21. ICLR 2026Generalized Parallel Scaling with Interdependent Generations
22. ICLR 2026High-dimensional limit theorems for SGD: Momentum and Adaptive Step-sizes
23. ICLR 2026Hot Fuzz: Temperature-Tunable Composition of Diffusion models with Fuzzy Logic
24. ICLR 2026How Text Quality Interventions Reshape Neural Scaling Laws for LLMs: Empirical Study
25. ICLR 2026Implicit bias produces neural scaling laws in learning curves, from perceptrons to deep networks
26. ICLR 2026Intrinsic Entropy of Context Length Scaling in LLMs
27. ICLR 2026Intrinsic training dynamics of deep neural networks
28. ICLR 2026Inverse Reinforcement Learning with Dynamic Reward Scaling for LLM Alignment
29. ICLR 2026Learning Shrinks the Hard Tail: Training‑Dependent Inference Scaling in a Solvable Linear Model
30. ICLR 2026LipNeXt: Scaling up Lipschitz-based Certified Robustness to Billion-parameter Models
31. ICLR 2026Mitigating the Curse of Detail: Scaling Arguments for Feature Learning and Sample Complexity
32. ICLR 2026MOSS: Efficient and Accurate FP8 LLM Training with Microscaling and Automatic Scaling
33. ICLR 2026Pre-training under infinite compute
34. ICLR 2026Pretraining Scaling Laws for Generative Evaluations of Language Models
35. ICLR 2026Reverse Distillation: Disentangling and Scaling Protein Language Model Representations
36. ICLR 2026Revisiting the Scaling Properties of Downstream Metrics in Large Language Model Training
37. ICLR 2026ROC-n-reroll: How verifier imperfection affects test-time scaling
38. ICLR 2026Sample Complexity and Representation Ability of Test-time Scaling Paradigms
39. ICLR 2026Scaling Agent Learning via Experience Synthesis
40. ICLR 2026Scaling Agents via Continual Pre-training
41. ICLR 2026Scaling Attention via Feature Sparsity
42. ICLR 2026Scaling Behavior of Discrete Diffusion Language Models
43. ICLR 2026Scaling Direct Feedback Learning with Theoretical Guarantees
44. ICLR 2026Scaling Generalist Data-Analytic Agents
45. ICLR 2026Scaling Goal-conditioned Reinforcement Learning with Multistep Quasimetric Distances
46. ICLR 2026Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database
47. ICLR 2026Scaling Laws and Spectra of Shallow Neural Networks in the Feature Learning Regime
48. ICLR 2026Scaling Laws and Symmetry, Evidence from Neural Force Fields
49. ICLR 2026Scaling Laws for Diffusion Transformers
50. ICLR 2026Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs

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。