强化学习论文趋势 2023–2026
策略优化、离线 RL 与基于环境的决策学习
统计范围：NeurIPS、ICLR、ICML、CVPR 四个会议，2023–2026 年，共 6,885 篇被归入该主题的论文。
主要结论
- 强化学习论文从 2023 年的 1,070 篇增长到 2026 年的 2,985 篇，三年间扩大到约 2.8 倍。
- NeurIPS 是发强化学习论文最多的会议，2023–2026 年共 2,494 篇，占四个会议总量的 36%。
- 交叉方向里涨得最快的是推理模型：同时属于两个主题的论文从 2023 年的 9 篇增至 2026 年的 437 篇。
各会议论文数
按年份和会议统计
| 年份 | NeurIPS | ICLR | ICML | CVPR | 合计 |
|---|---|---|---|---|---|
| 2023 | 484 | 233 | 297 | 56 | 1,070 |
| 2024 | 466 | 304 | 356 | 64 | 1,190 |
| 2025 | 784 | 379 | 405 | 72 | 1,640 |
| 2026 | 760 | 911 | 1,021 | 293 | 2,985 |
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 2026\texttt{FEROM}: Frontier Endogenous Reveal-Order Marginal Policy Optimization for Masked Diffusion LMs
- ICML 2026$\text{DT}^\text{2}$: Decision-Targeted Digital Twins
- ICLR 2026$\nabla$-Reasoner: LLM Reasoning via Test-Time Gradient Descent in Textual Space
- CVPR 2026A Causal Marriage between VLM and IRM from Understanding to
- NeurIPS 2025$\texttt{G1}$: Teaching LLMs to Reason on Graphs with Reinforcement Learning
- ICML 2025A Computationally Efficient Algorithm for Infinite-Horizon Average-Reward Linear MDPs
- ICLR 2025$\phi$-Update: A Class of Policy Update Methods with Policy Convergence Guarantee
- CVPR 2025All-Optical Nonlinear Diffractive Deep Network for Ultrafast Image Denoising
- NeurIPS 2024A Critical Evaluation of AI Feedback for Aligning Large Language Models
- ICML 2024${\rm E}(3)$-Equivariant Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning
- ICLR 2024A Black-box Approach for Non-stationary Multi-agent Reinforcement Learning
- CVPR 2024DMR: Decomposed Multi-Modality Representations for Frames and Events Fusion in Visual Reinforcement Learning
- NeurIPS 2023“Why Not Looking backward?” A Robust Two-Step Method to Automatically Terminate Bayesian Optimization
- ICML 2023A Connection between One-Step RL and Critic Regularization in Reinforcement Learning
- ICLR 2023$O(T^{-1})$ Convergence of Optimistic-Follow-the-Regularized-Leader in Two-Player Zero-Sum Markov Games
- CVPR 2023"Seeing" Electric Network Frequency From Events
- NeurIPS 2026$\varphi$TD: Distributional Reinforcement Learning using Characteristic Functions
- ICML 2026$\textit{S}$-SPPO: Semantic-Calibrated Self-Play Preference Optimization
- ICLR 2026$AutoDrive\text{-}P^3$: Unified Chain of Perception–Prediction–Planning Thought via Reinforcement Fine-Tuning
- CVPR 2026AdaptVision Efficient Vision-Language Models via Adaptive Visual Acquisition
全部强化学习论文
共 2,494 篇，第 46 / 50 页
- NeurIPS 2023Learning Time-Invariant Representations for Individual Neurons from Population Dynamics
- NeurIPS 2023Learning to Discover Skills through Guidance
- NeurIPS 2023Learning to Influence Human Behavior with Offline Reinforcement Learning
- NeurIPS 2023Learning to Modulate pre-trained Models in RL
- NeurIPS 2023Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Opt
- NeurIPS 2023Learning Unseen Modality Interaction
- NeurIPS 2023Learning World Models with Identifiable Factorization
- NeurIPS 2023Lending Interaction Wings to Recommender Systems with Conversational Agents
- NeurIPS 2023LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios
- NeurIPS 2023Likelihood Ratio Confidence Sets for Sequential Decision Making
- NeurIPS 2023LIMA: Less Is More for Alignment
- NeurIPS 2023List and Certificate Complexities in Replicable Learning
- NeurIPS 2023LogSpecT: Feasible Graph Learning Model from Stationary Signals with Recovery Guarantees
- NeurIPS 2023Long-Term Fairness with Unknown Dynamics
- NeurIPS 2023Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline RL
- NeurIPS 2023Loss Dynamics of Temporal Difference Reinforcement Learning
- NeurIPS 2023Macro Placement by Wire-Mask-Guided Black-Box Optimization
- NeurIPS 2023MAG-GNN: Reinforcement Learning Boosted Graph Neural Network
- NeurIPS 2023Making Scalable Meta Learning Practical
- NeurIPS 2023Managing Temporal Resolution in Continuous Value Estimation: A Fundamental Trade-off
- NeurIPS 2023Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration
- NeurIPS 2023Maximum State Entropy Exploration using Predecessor and Successor Representations
- NeurIPS 2023MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning
- NeurIPS 2023MIMEx: Intrinsic Rewards from Masked Input Modeling
- NeurIPS 2023Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks
- NeurIPS 2023Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms
- NeurIPS 2023Model-Free Active Exploration in Reinforcement Learning
- NeurIPS 2023Model-free Posterior Sampling via Learning Rate Randomization
- NeurIPS 2023Model-Free Reinforcement Learning with the Decision-Estimation Coefficient
- NeurIPS 2023Monte Carlo Tree Search with Boltzmann Exploration
- NeurIPS 2023MoVie: Visual Model-Based Policy Adaptation for View Generalization
- NeurIPS 2023Multi-Agent First Order Constrained Optimization in Policy Space
- NeurIPS 2023Multi-Agent Meta-Reinforcement Learning: Sharper Convergence Rates with Task Similarity
- NeurIPS 2023Multi-Modal Inverse Constrained Reinforcement Learning from a Mixture of Demonstrations
- NeurIPS 2023Multi-Objective Intrinsic Reward Learning for Conversational Recommender Systems
- NeurIPS 2023Multi-Step Generalized Policy Improvement by Leveraging Approximate Models
- NeurIPS 2023Mutual Information Regularized Offline Reinforcement Learning
- NeurIPS 2023Mutual-Information Regularized Multi-Agent Policy Iteration
- NeurIPS 2023Natural Actor-Critic for Robust Reinforcement Learning with Function Approximation
- NeurIPS 2023Natural Language Instruction-following with Task-related Language Development and Translation
- NeurIPS 2023Nearly Tight Bounds For Differentially Private Multiway Cut
- NeurIPS 2023Necessary and Sufficient Conditions for Optimal Decision Trees using Dynamic Programming
- NeurIPS 2023NetHack is Hard to Hack
- NeurIPS 2023Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning
- NeurIPS 2023Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement
- NeurIPS 2023No-Regret Online Reinforcement Learning with Adversarial Losses and Transitions
- NeurIPS 2023Noether Embedding: Efficient Learning of Temporal Regularities
- NeurIPS 2023Non-Convex Bilevel Optimization with Time-Varying Objective Functions
- NeurIPS 2023NVFi: Neural Velocity Fields for 3D Physics Learning from Dynamic Videos
- NeurIPS 2023ODE-based Recurrent Model-free Reinforcement Learning for POMDPs
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 查强化学习在 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": "reinforcement-learning"}
常见问题
- 2023–2026 年四大会议一共有多少篇强化学习论文？
- 共 6,885 篇，按年份分别是 2023 年 1,070 篇、2024 年 1,190 篇、2025 年 1,640 篇、2026 年 2,985 篇。
- NeurIPS 2026 有多少篇强化学习论文？
- NeurIPS 2026 有 760 篇强化学习论文，上一届是 784 篇。
- ICLR 2026 有多少篇强化学习论文？
- ICLR 2026 有 911 篇强化学习论文，上一届是 379 篇。
- ICML 2026 有多少篇强化学习论文？
- ICML 2026 有 1,021 篇强化学习论文，上一届是 405 篇。
- CVPR 2026 有多少篇强化学习论文？
- CVPR 2026 有 293 篇强化学习论文，上一届是 72 篇。
- 哪个会议的强化学习论文最多？
- NeurIPS，2023–2026 年共 2,494 篇。