强化学习论文趋势 2023–2026

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。

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 297 篇，第 4 / 6 页

151. ICML 2023Meta Optimal Transport
152. ICML 2023Meta-learning Parameterized Skills
153. ICML 2023Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization
154. ICML 2023MetaDiffuser: Diffusion Model as Conditional Planner for Offline Meta-RL
155. ICML 2023Model Transferability with Responsive Decision Subjects
156. ICML 2023Model-agnostic Measure of Generalization Difficulty
157. ICML 2023Model-based Offline Reinforcement Learning with Count-based Conservatism
158. ICML 2023Model-based Reinforcement Learning with Scalable Composite Policy Gradient Estimators
159. ICML 2023Model-Bellman Inconsistency for Model-based Offline Reinforcement Learning
160. ICML 2023Model-Free Robust Average-Reward Reinforcement Learning
161. ICML 2023Multi-Agent Learning from Learners
162. ICML 2023Multi-Environment Pretraining Enables Transfer to Action Limited Datasets
163. ICML 2023Multi-task Hierarchical Adversarial Inverse Reinforcement Learning
164. ICML 2023Multi-Task Off-Policy Learning from Bandit Feedback
165. ICML 2023Multi-User Reinforcement Learning with Low Rank Rewards
166. ICML 2023Multiply Robust Off-policy Evaluation and Learning under Truncation by Death
167. ICML 2023N$\text{A}^\text{2}$Q: Neural Attention Additive Model for Interpretable Multi-Agent Q-Learning
168. ICML 2023Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR
169. ICML 2023Near-Optimal $\Phi$-Regret Learning in Extensive-Form Games
170. ICML 2023Near-optimal Conservative Exploration in Reinforcement Learning under Episode-wise Constraints
171. ICML 2023Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes
172. ICML 2023Nested Elimination: A Simple Algorithm for Best-Item Identification From Choice-Based Feedback
173. ICML 2023Neural Collapse in Deep Linear Networks: From Balanced to Imbalanced Data
174. ICML 2023Non-stationary Reinforcement Learning under General Function Approximation
175. ICML 2023Off-Policy Average Reward Actor-Critic with Deterministic Policy Search
176. ICML 2023Offline Learning in Markov Games with General Function Approximation
177. ICML 2023Offline Meta Reinforcement Learning with In-Distribution Online Adaptation
178. ICML 2023Offline Reinforcement Learning with Closed-Form Policy Improvement Operators
179. ICML 2023On Penalty-based Bilevel Gradient Descent Method
180. ICML 2023On the Convergence of SARSA with Linear Function Approximation
181. ICML 2023On the Effectiveness of Offline RL for Dialogue Response Generation
182. ICML 2023On the Global Convergence of Risk-Averse Policy Gradient Methods with Expected Conditional Risk Measures
183. ICML 2023On the Importance of Feature Decorrelation for Unsupervised Representation Learning in Reinforcement Learning
184. ICML 2023On the Power of Pre-training for Generalization in RL: Provable Benefits and Hardness
185. ICML 2023One-sided Matrix Completion from Two Observations Per Row
186. ICML 2023Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning
187. ICML 2023Optimal Horizon-Free Reward-Free Exploration for Linear Mixture MDPs
188. ICML 2023Optimizing DDPM Sampling with Shortcut Fine-Tuning
189. ICML 2023Oracles & Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement Learning
190. ICML 2023PAC-Bayesian Offline Contextual Bandits With Guarantees
191. ICML 2023Parallel $Q$-Learning: Scaling Off-policy Reinforcement Learning under Massively Parallel Simulation
192. ICML 2023Partially Observable Multi-agent RL with (Quasi-)Efficiency: The Blessing of Information Sharing
193. ICML 2023Performative Reinforcement Learning
194. ICML 2023PFNs4BO: In-Context Learning for Bayesian Optimization
195. ICML 2023Policy Contrastive Imitation Learning
196. ICML 2023Policy Evaluation and Temporal-Difference Learning in Continuous Time and Space: A Martingale Approach
197. ICML 2023Policy Gradient in Robust MDPs with Global Convergence Guarantee
198. ICML 2023Policy Regularization with Dataset Constraint for Offline Reinforcement Learning
199. ICML 2023Posterior Sampling for Deep Reinforcement Learning
200. ICML 2023PPG Reloaded: An Empirical Study on What Matters in Phasic Policy Gradient

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。