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

每年论文数

各会议论文数

按年份和会议统计

缩放定律论文数，按年份和会议
年份NeurIPSICLRICMLCVPR合计
2023211417860
20243815331096
202580485617201
202682737815248

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

全部缩放定律论文

年份全部2023202420252026
会议全部NeurIPSICLRICMLCVPR

共 605 篇，第 5 / 13 页

  1. NeurIPS 2026Normalized Architectures are Natively 4-Bit
  2. NeurIPS 2026OmniDex: Scaling Dexterous Hand Grasping to Diverse Cluttered Scenes
  3. NeurIPS 2026On Data Engineering for Scaling LLM Terminal Capabilities
  4. NeurIPS 2026On Neural Scaling Laws for Weather Emulation through Continual Training
  5. NeurIPS 2026On the Nonlinearity of Learning Rate Scaling for LLM Training
  6. NeurIPS 2026On the Recall Scaling Laws in Mamba: A Theoretical and Mechanistic Study via Hashing
  7. NeurIPS 2026Optimal scaling laws in learning hierarchical multi-index models
  8. NeurIPS 2026Overcoming Kernel Redundancy for Scaling Logic Gate Networks
  9. NeurIPS 2026Predicting and improving test-time scaling laws via reward tail-guided search
  10. NeurIPS 2026Problem-Dependent Dynamic Regret over a Predictor Class with One-Gradient Feedback
  11. NeurIPS 2026Provable Test-Time Scaling for Beam Search in LLM Reasoning
  12. NeurIPS 2026Random Attention Pattern Learning Enables Emergent Capabilities
  13. NeurIPS 2026RaZeR: Pushing the Limits of NVFP4 Quantization with Redundant Zero Remapping
  14. NeurIPS 2026Reasoning Warm-up: Scaling Label-free RL via Verifiable Surrogate Rewards
  15. NeurIPS 2026Reference-Guided Training: Adaptive Gradient Scaling via Per-Sample Loss Comparisons
  16. NeurIPS 2026Reinforce Adjoint Matching: Scaling RL Post-Training of Diffusion and Flow Models
  17. NeurIPS 2026Rethinking Language Model Scaling under Transferable Hypersphere Optimization
  18. NeurIPS 2026Scale Where It Matters: Training-Free Localized Scaling for Diffusion Models
  19. NeurIPS 2026Scaling Arbitrary Architectures and Optimizers with Automatic Parameterization
  20. NeurIPS 2026Scaling Genomic Language Modeling with Unified Corpus and Evaluation in Bacteria
  21. NeurIPS 2026Scaling Laws and Tradeoffs in Recurrent Networks of Expressive Neurons
  22. NeurIPS 2026Scaling Laws for SAE Training Data
  23. NeurIPS 2026Scaling Linear Mode Connectivity and Merging to Billion Parameter Pretrained Transformers
  24. NeurIPS 2026Scaling Neural Motor Decoding via Decoupled Behavioral Pretraining
  25. NeurIPS 2026Scaling Optimization-Oriented Hypernetworks for Implicit Neural Representations
  26. NeurIPS 2026Scaling Storm-Resolving Atmospheric AI Simulation to the Entire Planet
  27. NeurIPS 2026SDS-LoRA: Overcoming Anisotropic Gradient Scaling in Low-Rank Adaptation
  28. NeurIPS 2026Selected-Tail Reliability in Verifier-Guided Best-of-$N$ Inference
  29. NeurIPS 2026SETA: Scaling Environments for Terminal Agents
  30. NeurIPS 2026Sharp Capacity Scaling of Spectral Optimizers in Learning Associative Memory
  31. NeurIPS 2026Sharp feature-learning transitions and Bayes-optimal neural scaling laws in extensive-width networks
  32. NeurIPS 2026Sharpness, Stability, and Step-Size Scaling in Deep Polynomial Networks
  33. NeurIPS 2026SWE-Crafter: Scaling Executable Multilingual Software Engineering Data with Meta-Skill Agents
  34. NeurIPS 2026Systematic Scaling Analysis of Jailbreak Attacks in Large Language Models
  35. NeurIPS 2026Tail Competition Explains Scaling in Best-of-N Sampling for Verifiable Problems
  36. NeurIPS 2026Target-Budget Best-Function Identification for Scaling-Law-Guided Model Selection
  37. NeurIPS 2026Test-time Scaling of Diffusions with Flow Maps
  38. NeurIPS 2026Test-Time Scaling with Diffusion Language Models via Reward-Guided Stitching
  39. NeurIPS 2026The Geometric Wall: Manifold Structure Predicts Layerwise Sparse Autoencoder Scaling Laws
  40. NeurIPS 2026The Hidden Power of Scaling Factor in LoRA Optimization
  41. NeurIPS 2026The Minimax Rate of Second-Order Calibration
  42. NeurIPS 2026The Scaling Laws of Skills in LLM Agent Systems
  43. NeurIPS 2026Theory of Optimal Learning Rate Schedules and Scaling Laws for a Random Feature Model
  44. NeurIPS 2026Tokens-per-Parameter Coverage Is Critical for Robust LLM Scaling Law Extrapolation
  45. NeurIPS 2026Training Optimal Large Diffusion Language Models
  46. NeurIPS 2026What should post-training optimize? A test-time scaling law perspective
  47. NeurIPS 2026When Less is More: The LLM Scaling Paradox in Context Compression
  48. NeurIPS 2026When Reasoning Meets Its Laws
  49. CVPR 2025Accurate Scene Text Recognition with Efficient Model Scaling and Cloze Self-Distillation
  50. CVPR 2025Cubify Anything: Scaling Indoor 3D Object Detection

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"}

两分钟接入 Paperlist MCP（Claude Code、Cursor、Codex、ChatGPT）

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。

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