高效训练论文趋势 2023–2026
Scaling law、MoE、蒸馏与训练效率
统计范围:NeurIPS、ICLR、ICML、CVPR 四个会议,2023–2026 年,共 1,765 篇被归入该主题的论文。
主要结论
- 高效训练论文从 2023 年的 346 篇增长到 2026 年的 603 篇,三年间扩大到约 1.7 倍。
- NeurIPS 是发高效训练论文最多的会议,2023–2026 年共 499 篇,占四个会议总量的 28%。
- 交叉方向里涨得最快的是深度学习 · large language models:同时属于两个主题的论文从 2023 年的 6 篇增至 2026 年的 50 篇。
- 在「效率与系统」的 5 个主题里,它的 2026 年论文数排第 3;同组增长最快的硬件协同是 4.4 倍,高效训练是 1.7 倍。
各会议论文数
按年份和会议统计
| 年份 | NeurIPS | ICLR | ICML | CVPR | 合计 |
|---|---|---|---|---|---|
| 2023 | 112 | 67 | 83 | 84 | 346 |
| 2024 | 20 | 78 | 122 | 80 | 300 |
| 2025 | 190 | 126 | 118 | 82 | 516 |
| 2026 | 177 | 148 | 169 | 109 | 603 |
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 2026Adaptive Residual Quantization for Memory-Efficient Temporal Action Segmentation
- ICML 2026A Geometry-Aware Efficient Algorithm for Compositional Entropic Risk Minimization
- ICLR 2026An Efficient SE(p)-Invariant Transport Metric Driven by Polar Transport Discrepancy-based Representation
- CVPR 2026A Bit is All You Need Efficient Video Capture via
- NeurIPS 20254DGCPro: Efficient Hierarchical 4D Gaussian Compression for Progressive Volumetric Video Streaming
- ICML 2025A Sample Efficient Conditional Independence Test in the Presence of Discretization
- ICLR 2025Breaking Class Barriers: Efficient Dataset Distillation via Inter-Class Feature Compensator
- CVPR 2025Accurate Scene Text Recognition with Efficient Model Scaling and Cloze Self-Distillation
- NeurIPS 2024$\texttt{dattri}$: A Library for Efficient Data Attribution
- ICML 2024A fast algorithm to simulate nonlinear resistive networks
- ICLR 2024Dictionary Contrastive Learning for Efficient Local Supervision without Auxiliary Networks
- CVPR 2024Deep-TROJ: An Inference Stage Trojan Insertion Algorithm through Efficient Weight Replacement Attack
- NeurIPS 2023Alternating Updates for Efficient Transformers
- ICML 2023Auxiliary Modality Learning with Generalized Curriculum Distillation
- ICLR 2023A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning
- CVPR 2023A Rotation-Translation-Decoupled Solution for Robust and Efficient Visual-Inertial Initialization
- NeurIPS 2026Advanced Routing as Regularization Allocation for Efficient Diffusion Transformer Training
- ICML 2026Active Learning with Foundation Model Priors: Efficient Learning under Class Imbalance
- ICLR 2026AQER: A Scalable and Efficient Data Loader for Digital Quantum Computers
- CVPR 2026Adaptive Bayesian Early-Exit Networks for Efficient Non-Transferable Learning
全部高效训练论文
共 1,765 篇,第 9 / 36 页
- ICML 2026Sparser, Faster, Lighter Transformer Language Models
- ICML 2026SparseSSM: Efficient Selective Structured State Space Models Can Be Pruned in One-Shot
- ICML 2026Spatial-Aware Reduction Framework: Towards Efficient and Faithful Visual State Space Models
- ICML 2026SpecForge: A Flexible and Efficient Open-Source Training Framework for Speculative Decoding
- ICML 2026Spectral Reach: Understanding Neural Scaling as Progress into the Spectral Tail
- ICML 2026Spik4lite: Refactoring Neuromorphic Sparsity for Efficient Spiking Neural Networks on Commodity Edge Devices
- ICML 2026SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space
- ICML 2026Swordsman: Entropy-Driven Adaptive Block Partition for Efficient Diffusion Language Models
- ICML 2026TabPack: Efficient Hyperparameter Ensembles for Tabular Deep Learning
- ICML 2026TetraJet-v2: Accurate NVFP4 Training for Large Language Models with Oscillation Suppression and Outlier Control
- ICML 2026ToaSt: Token Channel Selection and Structured Pruning for Efficient ViT
- ICML 2026Token Sparse Attention: Efficient Long-Context Inference with Interleaved Token Selection
- ICML 2026TokenDrop: Token-Level Importance-Aware Backward Propagation Skipping for Efficient LLM Fine-Tuning
- ICML 2026Towards Resource-Efficient LLMs: End-to-End Energy Accounting of Distillation Pipelines
- ICML 2026Training-Free Hashing-Based Attention via Binary Principal Components
- ICML 2026Turning Back Without Forgetting: Selective Backward Refinement for Parameter-Efficient Continual Learning
- ICML 2026Turning Stale Gradients into Stable Gradients: Coherent Coordinate Descent with Implicit Landscape Smoothing for Lightweight Zeroth-Order Optimization
- ICML 2026UniFast-HGR: Scalable and Efficient Maximal Correlation for Multimodal Models
- ICML 2026Unifying Dataset Pruning and Distillation for Efficient Large-scale Compression
- ICML 2026Unifying Stacking and Cascading for Efficient Ensemble Inference
- ICML 2026Universal One-third Time Scaling in Learning Peaked Distributions
- ICML 2026Untied Ulysses: Memory-Efficient Context Parallelism via Headwise Chunking
- ICML 2026Utility Boundary of Dataset Distillation: Scaling and Coverage Laws
- ICML 2026WAVE: Window-Aware Vocabulary-Efficient Early-Exit for Training-Free LLM Acceleration
- ICML 2026WBMM: Windowed Batch Matrix Multiplication for Efficient Large Receptive Field Convolution
- ICML 2026ZipMoE: Efficient On-Device MoE Serving via Lossless Compression and Cache-Affinity Scheduling
- NeurIPS 2026$\delta$-Mem: : Efficient Online Memory For Large Language Models
- NeurIPS 2026A Grid Efficient Transport Optimization Model for the Wasserstein Barycenter Problem
- NeurIPS 2026A Solver-Efficient Neural Adversarial Attack on Subgraph Matching Models
- NeurIPS 2026Achieve Latency-Efficient Temporal-Coding Spiking LLMs via Discretization-Aware Conversion
- NeurIPS 2026Adapting in the Dark: Efficient and Stable Test-Time Adaptation for Black-Box Models
- NeurIPS 2026Adaptive Residual Quantization for Memory-Efficient Temporal Action Segmentation
- NeurIPS 2026ADIS-Law: Unified Scaling Laws for Annealing-Phase Domain Injection in Large Language Models
- NeurIPS 2026Advanced Routing as Regularization Allocation for Efficient Diffusion Transformer Training
- NeurIPS 2026AgentWeave: Efficient Distributed Agent Serving via Flow Decomposition
- NeurIPS 2026An Efficient Cross-modal Feature Reconstruction Model for Multimodal Multi-class Anomaly Detection
- NeurIPS 2026ASVQ: What Reparameterization Is Just Enough for Efficient Codebook Learning?
- NeurIPS 2026Asymmetric Scaling Laws from Sparse Features
- NeurIPS 2026Attribute-Efficient Learning of Sparse Halfspaces with Constant Malicious Noise Rate
- NeurIPS 2026Benchmarks as Measurement Instruments: Quantifying Signal and Noise for More Efficient AI Evaluations Under Distribution Shift
- NeurIPS 2026Beyond Pairwise Supervision: Spectral Characteristic Matching for Data-Efficient Multimodal Alignment
- NeurIPS 2026Beyond the Half Approximation: Fair and Efficient Online Class Matching
- NeurIPS 2026Bidirectional Information Flow (BIF) - A Sample Efficient Hierarchical Gaussian Process for Bayesian Optimization
- NeurIPS 2026BiHashFormer: Hash-Driven Dual-Branch Transformer for Efficient Object Detection in HRW Shots
- NeurIPS 2026BLANP: Memory-Efficient Backpropagation-Free Local Training via Antithetic Node Perturbation
- NeurIPS 2026BROS: Bias-Corrected Randomized Subspaces for Memory-Efficient Single-Loop Bilevel Optimization
- NeurIPS 2026ByteDistill: Cross-Tokenizer Distillation via Chunk-wise Byte-Level Distribution Alignment
- NeurIPS 2026CELEUS: Certifiable and Efficient LLM Evaluation via E-Processes
- NeurIPS 2026CHoRD: Coordinating Scheduling and Data Placement for Efficient Deep Neural Network Inference on Chiplet-Based GPUs
- NeurIPS 2026Communication-Efficient LLM Adaptation over Decentralized GPU Meshes
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 查高效训练在 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": "efficient-training"}
常见问题
- 2023–2026 年四大会议一共有多少篇高效训练论文?
- 共 1,765 篇,按年份分别是 2023 年 346 篇、2024 年 300 篇、2025 年 516 篇、2026 年 603 篇。
- NeurIPS 2026 有多少篇高效训练论文?
- NeurIPS 2026 有 177 篇高效训练论文,上一届是 190 篇。
- ICLR 2026 有多少篇高效训练论文?
- ICLR 2026 有 148 篇高效训练论文,上一届是 126 篇。
- ICML 2026 有多少篇高效训练论文?
- ICML 2026 有 169 篇高效训练论文,上一届是 118 篇。
- CVPR 2026 有多少篇高效训练论文?
- CVPR 2026 有 109 篇高效训练论文,上一届是 82 篇。
- 哪个会议的高效训练论文最多?
- NeurIPS,2023–2026 年共 499 篇。