通用机器学习 · reinforcement learning论文趋势 2023–2026

主要结论

• 通用机器学习 · reinforcement learning论文从 2023 年的 287 篇增长到 2026 年的 575 篇，三年间扩大到约 2.0 倍。
• ICLR 是发通用机器学习 · reinforcement learning论文最多的会议，2023–2026 年共 847 篇，占四个会议总量的 51%。
• 交叉方向里涨得最快的是扩散模型：同时属于两个主题的论文从 2023 年的 2 篇增至 2026 年的 34 篇。
• 在「通用机器学习」的 6 个主题里，它的 2026 年论文数排第 2；同组增长最快的通用机器学习 · evaluation是 4.3 倍，通用机器学习 · reinforcement learning是 2.0 倍。

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 2026The Sample Complexity of Multiple Change Point Identification under Bandit Feedback
• ICML 2026De-attribute to Forget for LLM Unlearning
• ICLR 2026AbstRaL: Augmenting LLMs' Reasoning by Reinforcing Abstract Thinking
• CVPR 2026Dynamic Important Example Mining for Reinforcement Finetuning
• NeurIPS 2025RULE: Reinforcement UnLEarning Achieves Forget-retain Pareto Optimality
• ICML 2025The Number of Trials Matters in Infinite-Horizon General-Utility Markov Decision Processes
• ICLR 2025A General Framework for Off-Policy Learning with Partially-Observed Reward
• CVPR 2025Automated Proof of Polynomial Inequalities via Reinforcement Learning
• NeurIPS 2024Learning General Parameterized Policies for Infinite Horizon Average Reward Constrained MDPs via Primal-Dual Policy Gradient Algorithm
• ICML 2024Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation
• ICLR 2024Bandits with Replenishable Knapsacks: the Best of both Worlds
• CVPR 2024Improving Unsupervised Hierarchical Representation with Reinforcement Learning
• NeurIPS 2023General Munchausen Reinforcement Learning with Tsallis Kullback-Leibler Divergence
• ICML 2023Non-stationary Reinforcement Learning under General Function Approximation
• ICLR 2023A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning
• CVPR 2023Frustratingly Easy Regularization on Representation Can Boost Deep Reinforcement Learning
• NeurIPS 2026Learning When to Stop: Selective Imitation Learning Under Arbitrary Dynamics Shift
• ICML 2026Fox in the Henhouse: Supply-Chain Backdoor Attacks Against Reinforcement Learning
• ICLR 2026APPLE: Toward General Active Perception via Reinforcement Learning
• CVPR 2026Reinforcement-Guided Synthetic Data Generation for Privacy-Sensitive Identity Recognition

全部通用机器学习 · reinforcement learning论文

共 287 篇，第 3 / 6 页

101. ICML 2023Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR
102. ICML 2023Near-optimal Conservative Exploration in Reinforcement Learning under Episode-wise Constraints
103. ICML 2023Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes
104. ICML 2023Non-stationary Reinforcement Learning under General Function Approximation
105. ICML 2023Offline Reinforcement Learning with Closed-Form Policy Improvement Operators
106. ICML 2023Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning
107. ICML 2023Parallel $Q$-Learning: Scaling Off-policy Reinforcement Learning under Massively Parallel Simulation
108. ICML 2023Performative Reinforcement Learning
109. ICML 2023Policy Regularization with Dataset Constraint for Offline Reinforcement Learning
110. ICML 2023Posterior Sampling for Deep Reinforcement Learning
111. ICML 2023Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons
112. ICML 2023Provable Reset-free Reinforcement Learning by No-Regret Reduction
113. ICML 2023Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources
114. ICML 2023Reachability-Aware Laplacian Representation in Reinforcement Learning
115. ICML 2023Reinforcement Learning Can Be More Efficient with Multiple Rewards
116. ICML 2023Reinforcement Learning from Passive Data via Latent Intentions
117. ICML 2023Reinforcement Learning in Low-rank MDPs with Density Features
118. ICML 2023Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space
119. ICML 2023Reinforcement Learning with History Dependent Dynamic Contexts
120. ICML 2023ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs
121. ICML 2023Representation-Driven Reinforcement Learning
122. ICML 2023Revisiting the Linear-Programming Framework for Offline RL with General Function Approximation
123. ICML 2023Robust Situational Reinforcement Learning in Face of Context Disturbances
124. ICML 2023Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories
125. ICML 2023Set-membership Belief State-based Reinforcement Learning for POMDPs
126. ICML 2023Simple Embodied Language Learning as a Byproduct of Meta-Reinforcement Learning
127. ICML 2023Simplified Temporal Consistency Reinforcement Learning
128. ICML 2023SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement Learning
129. ICML 2023Subequivariant Graph Reinforcement Learning in 3D Environments
130. ICML 2023Supported Trust Region Optimization for Offline Reinforcement Learning
131. ICML 2023TGRL: An Algorithm for Teacher Guided Reinforcement Learning
132. ICML 2023The Benefits of Model-Based Generalization in Reinforcement Learning
133. ICML 2023The Dormant Neuron Phenomenon in Deep Reinforcement Learning
134. ICML 2023The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation
135. ICML 2023The Unintended Consequences of Discount Regularization: Improving Regularization in Certainty Equivalence Reinforcement Learning
136. ICML 2023Towards Theoretical Understanding of Inverse Reinforcement Learning
137. ICML 2023Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning
138. ICML 2023Truncating Trajectories in Monte Carlo Reinforcement Learning
139. ICML 2023Variance Control for Distributional Reinforcement Learning
140. ICML 2023What can online reinforcement learning with function approximation benefit from general coverage conditions?
141. ICML 2023When is Realizability Sufficient for Off-Policy Reinforcement Learning?
142. NeurIPS 2023A Definition of Continual Reinforcement Learning
143. NeurIPS 2023A Novel Framework for Policy Mirror Descent with General Parameterization and Linear Convergence
144. NeurIPS 2023A Reduction-based Framework for Sequential Decision Making with Delayed Feedback
145. NeurIPS 2023A State Representation for Diminishing Rewards
146. NeurIPS 2023Accelerating Exploration with Unlabeled Prior Data
147. NeurIPS 2023Accelerating Monte Carlo Tree Search with Probability Tree State Abstraction
148. NeurIPS 2023Accelerating Reinforcement Learning with Value-Conditional State Entropy Exploration
149. NeurIPS 2023Accelerating Value Iteration with Anchoring
150. NeurIPS 2023Accountability in Offline Reinforcement Learning: Explaining Decisions with a Corpus of Examples

常见问题

2023–2026 年四大会议一共有多少篇通用机器学习 · reinforcement learning论文？

共 1,676 篇，按年份分别是 2023 年 287 篇、2024 年 403 篇、2025 年 411 篇、2026 年 575 篇。

NeurIPS 2026 有多少篇通用机器学习 · reinforcement learning论文？

NeurIPS 2026 有 113 篇通用机器学习 · reinforcement learning论文，上一届是 108 篇。

ICLR 2026 有多少篇通用机器学习 · reinforcement learning论文？

ICLR 2026 有 335 篇通用机器学习 · reinforcement learning论文，上一届是 230 篇。

ICML 2026 有多少篇通用机器学习 · reinforcement learning论文？

ICML 2026 有 113 篇通用机器学习 · reinforcement learning论文，上一届是 71 篇。

CVPR 2026 有多少篇通用机器学习 · reinforcement learning论文？

CVPR 2026 有 14 篇通用机器学习 · reinforcement learning论文，上一届是 2 篇。

哪个会议的通用机器学习 · reinforcement learning论文最多？

ICLR，2023–2026 年共 847 篇。