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 6,885 篇，第 106 / 138 页
- ICML 2024Quality-Diversity with Limited Resources
- ICML 2024Quality-Weighted Vendi Scores And Their Application To Diverse Experimental Design
- ICML 2024Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency
- ICML 2024Random Latent Exploration for Deep Reinforcement Learning
- ICML 2024Rate-Optimal Policy Optimization for Linear Markov Decision Processes
- ICML 2024Recovering Labels from Local Updates in Federated Learning
- ICML 2024ReDiffuser: Reliable Decision-Making Using a Diffuser with Confidence Estimation
- ICML 2024Refining Minimax Regret for Unsupervised Environment Design
- ICML 2024Reflective Policy Optimization
- ICML 2024Reinforcement Learning and Regret Bounds for Admission Control
- ICML 2024Reinforcement Learning from Reachability Specifications: PAC Guarantees with Expected Conditional Distance
- ICML 2024Reinforcement Learning within Tree Search for Fast Macro Placement
- ICML 2024Reinformer: Max-Return Sequence Modeling for Offline RL
- ICML 2024ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages
- ICML 2024ReMax: A Simple, Effective, and Efficient Reinforcement Learning Method for Aligning Large Language Models
- ICML 2024Remembering to Be Fair: Non-Markovian Fairness in Sequential Decision Making
- ICML 2024Removing Spurious Concepts from Neural Network Representations via Joint Subspace Estimation
- ICML 2024Rethinking Decision Transformer via Hierarchical Reinforcement Learning
- ICML 2024Rethinking Transformers in Solving POMDPs
- ICML 2024Revisiting Inexact Fixed-Point Iterations for Min-Max Problems: Stochasticity and Structured Nonconvexity
- ICML 2024Revisiting Scalable Hessian Diagonal Approximations for Applications in Reinforcement Learning
- ICML 2024Reward Model Learning vs. Direct Policy Optimization: A Comparative Analysis of Learning from Human Preferences
- ICML 2024Reward Shaping for Reinforcement Learning with An Assistant Reward Agent
- ICML 2024Reward-Free Kernel-Based Reinforcement Learning
- ICML 2024Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment
- ICML 2024RICE: Breaking Through the Training Bottlenecks of Reinforcement Learning with Explanation
- ICML 2024Rich-Observation Reinforcement Learning with Continuous Latent Dynamics
- ICML 2024RIME: Robust Preference-based Reinforcement Learning with Noisy Preferences
- ICML 2024Risk-Sensitive Policy Optimization via Predictive CVaR Policy Gradient
- ICML 2024Risk-Sensitive Reward-Free Reinforcement Learning with CVaR
- ICML 2024RL-CFR: Improving Action Abstraction for Imperfect Information Extensive-Form Games with Reinforcement Learning
- ICML 2024RL-VLM-F: Reinforcement Learning from Vision Language Foundation Model Feedback
- ICML 2024RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback
- ICML 2024RLVF: Learning from Verbal Feedback without Overgeneralization
- ICML 2024RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation
- ICML 2024Robust Inverse Constrained Reinforcement Learning under Model Misspecification
- ICML 2024Robust Optimization in Protein Fitness Landscapes Using Reinforcement Learning in Latent Space
- ICML 2024Run-Time Task Composition with Safety Semantics
- ICML 2024RVI-SAC: Average Reward Off-Policy Deep Reinforcement Learning
- ICML 2024Safe Reinforcement Learning using Finite-Horizon Gradient-based Estimation
- ICML 2024Sample Average Approximation for Conditional Stochastic Optimization with Dependent Data
- ICML 2024Sample-Efficient Multiagent Reinforcement Learning with Reset Replay
- ICML 2024Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty
- ICML 2024SAPG: Split and Aggregate Policy Gradients
- ICML 2024Scalable Multiple Kernel Clustering: Learning Clustering Structure from Expectation
- ICML 2024Scalable Online Exploration via Coverability
- ICML 2024Seizing Serendipity: Exploiting the Value of Past Success in Off-Policy Actor-Critic
- ICML 2024Self-Composing Policies for Scalable Continual Reinforcement Learning
- ICML 2024SeMOPO: Learning High-quality Model and Policy from Low-quality Offline Visual Datasets
- ICML 2024Sequence Compression Speeds Up Credit Assignment in Reinforcement Learning
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。