缩放定律论文趋势 2023–2026
数据/模型缩放
统计范围:NeurIPS、ICLR、ICML、CVPR 四个会议,2023–2026 年,共 605 篇被归入该主题的论文。
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。
各会议论文数
按年份和会议统计
| 年份 | NeurIPS | ICLR | ICML | CVPR | 合计 |
|---|---|---|---|---|---|
| 2023 | 21 | 14 | 17 | 8 | 60 |
| 2024 | 38 | 15 | 33 | 10 | 96 |
| 2025 | 80 | 48 | 56 | 17 | 201 |
| 2026 | 82 | 73 | 78 | 15 | 248 |
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 605 篇,第 9 / 13 页
- NeurIPS 2025L$^2$M: Mutual Information Scaling Law for Long-Context Language Modeling
- NeurIPS 2025LCDB 1.1: A Database Illustrating Learning Curves Are More Ill-Behaved Than Previously Thought
- NeurIPS 2025Learning in Compact Spaces with Approximately Normalized Transformer
- NeurIPS 2025Learning quadratic neural networks in high dimensions: SGD dynamics and scaling laws
- NeurIPS 2025Meta CLIP 2: A Worldwide Scaling Recipe
- NeurIPS 2025Mind the GAP! The Challenges of Scale in Pixel-based Deep Reinforcement Learning
- NeurIPS 2025NaViL: Rethinking Scaling Properties of Native Multimodal Large Language Models under Data Constraints
- NeurIPS 2025NAVIX: Scaling MiniGrid Environments with JAX
- NeurIPS 2025On the Entropy Calibration of Language Models
- NeurIPS 2025On the Surprising Effectiveness of Large Learning Rates under Standard Width Scaling
- NeurIPS 2025Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model
- NeurIPS 2025Optimal and Provable Calibration in High-Dimensional Binary Classification: Angular Calibration and Platt Scaling
- NeurIPS 2025P-Law: Predicting Quantitative Scaling Law with Entropy Guidance in Large Recommendation Models
- NeurIPS 2025Parallel Scaling Law for Language Models
- NeurIPS 2025ParetoQ: Improving Scaling Laws in Extremely Low-bit LLM Quantization
- NeurIPS 2025Power Lines: Scaling laws for weight decay and batch size in LLM pre-training
- NeurIPS 2025Predictable Scale (Part II) --- Farseer: A Refined Scaling Law in LLMs
- NeurIPS 2025Provable Scaling Laws for the Test-Time Compute of Large Language Models
- NeurIPS 2025Recursive Inference Scaling: A Winning Path to Scalable Inference in Language and Multimodal Systems
- NeurIPS 2025Remasking Discrete Diffusion Models with Inference-Time Scaling
- NeurIPS 2025RigAnyFace: Scaling Neural Facial Mesh Auto-Rigging with Unlabeled Data
- NeurIPS 2025RoMA: Scaling up Mamba-based Foundation Models for Remote Sensing
- NeurIPS 2025Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection
- NeurIPS 2025Scaling and context steer LLMs along the same computational path as the human brain
- NeurIPS 2025Scaling can lead to compositional generalization
- NeurIPS 2025Scaling Embedding Layers in Language Models
- NeurIPS 2025Scaling Epidemic Inference on Contact Networks: Theory and Algorithms
- NeurIPS 2025Scaling Image Geo-Localization to Continent Level
- NeurIPS 2025Scaling Language-centric Omnimodal Representation Learning
- NeurIPS 2025Scaling Law with Learning Rate Annealing
- NeurIPS 2025Scaling Laws for Gradient Descent and Sign Descent for Linear Bigram Models under Zipf’s Law
- NeurIPS 2025Scaling Laws for Optimal Data Mixtures
- NeurIPS 2025Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets
- NeurIPS 2025Scaling Laws For Scalable Oversight
- NeurIPS 2025Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization
- NeurIPS 2025Scaling Offline RL via Efficient and Expressive Shortcut Models
- NeurIPS 2025Scaling Speculative Decoding with Lookahead Reasoning
- NeurIPS 2025Scaling Unlocks Broader Generation and Deeper Functional Understanding of Proteins
- NeurIPS 2025Scaling Up Active Testing to Large Language Models
- NeurIPS 2025Sloth: scaling laws for LLM skills to predict multi-benchmark performance across families
- NeurIPS 2025Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism
- NeurIPS 2025Superposition Yields Robust Neural Scaling
- NeurIPS 2025SWE-smith: Scaling Data for Software Engineering Agents
- NeurIPS 2025TabDPT: Scaling Tabular Foundation Models on Real Data
- NeurIPS 2025The Curse of Depth in Large Language Models
- NeurIPS 2025Understanding LLM Behaviors via Compression: Data Generation, Knowledge Acquisition and Scaling Laws
- NeurIPS 2025Unified Scaling Laws for Compressed Representations
- NeurIPS 2025When Worse is Better: Navigating the Compression Generation Trade-off In Visual Tokenization
- NeurIPS 2025Zero-Shot Performance Prediction for Probabilistic Scaling Laws
- CVPR 2024Alpha Invariance: On Inverse Scaling Between Distance and Volume Density in Neural Radiance Fields
在 Claude、Cursor 里直接问这个趋势
接上 Paperlist MCP 后,写综述、挑方向或准备投稿时,不用离开对话框就能拿到这页的数字:每年各会议的论文数、增长倍数、涨得最快的子方向和代表论文,AI 会按你的问题整理。
可以直接复制这句话问 AI
用 paperlist 查缩放定律在 NeurIPS 2023–2026 的论文趋势,哪一年涨得最快?
AI 会调用的工具:get_topic_stats {"topic": "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。