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 338 篇，第 4 / 7 页

151. ICML 2025KEA: Keeping Exploration Alive by Proactively Coordinating Exploration Strategies
152. ICML 2025Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network
153. ICML 2025Learning Policy Committees for Effective Personalization in MDPs with Diverse Tasks
154. ICML 2025Linear $Q$-Learning Does Not Diverge in $L^2$: Convergence Rates to a Bounded Set
155. ICML 2025Maximum Total Correlation Reinforcement Learning
156. ICML 2025Minimax Optimal Regret Bound for Reinforcement Learning with Trajectory Feedback
157. ICML 2025Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn
158. ICML 2025Overcoming the Curse of Dimensionality in Reinforcement Learning Through Approximate Factorization
159. ICML 2025Partially Observable Reinforcement Learning with Memory Traces
160. ICML 2025Policy Regularization on Globally Accessible States in Cross-Dynamics Reinforcement Learning
161. ICML 2025Polynomial-Time Approximability of Constrained Reinforcement Learning
162. ICML 2025Position: Constants are Critical in Regret Bounds for Reinforcement Learning
163. ICML 2025Position: General Intelligence Requires Reward-based Pretraining
164. ICML 2025Position: Lifetime tuning is incompatible with continual reinforcement learning
165. ICML 2025Provable Maximum Entropy Manifold Exploration via Diffusion Models
166. ICML 2025Provably Efficient Exploration in Inverse Constrained Reinforcement Learning
167. ICML 2025QPRL : Learning Optimal Policies with Quasi-Potential Functions for Asymmetric Traversal
168. ICML 2025Regret-Free Reinforcement Learning for Temporal Logic Specifications
169. ICML 2025Reinforcement Learning with Random Time Horizons
170. ICML 2025Reinforcement Learning with Segment Feedback
171. ICML 2025Reward Translation via Reward Machine in Semi-Alignable MDPs
172. ICML 2025Reward-Guided Prompt Evolving in Reinforcement Learning for LLMs
173. ICML 2025Robust Offline Reinforcement Learning with Linearly Structured $f$-Divergence Regularization
174. ICML 2025RobustZero: Enhancing MuZero Reinforcement Learning Robustness to State Perturbations
175. ICML 2025Sleeping Reinforcement Learning
176. ICML 2025Stay Hungry, Keep Learning: Sustainable Plasticity for Deep Reinforcement Learning
177. ICML 2025Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning
178. ICML 2025Task-Aware Virtual Training: Enhancing Generalization in Meta-Reinforcement Learning for Out-of-Distribution Tasks
179. ICML 2025The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning
180. ICML 2025The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks
181. ICML 2025The Number of Trials Matters in Infinite-Horizon General-Utility Markov Decision Processes
182. ICML 2025Universal Approximation Theorem of Deep Q-Networks
183. ICML 2025Unveiling Markov heads in Pretrained Language Models for Offline Reinforcement Learning
184. ICML 2025Vintix: Action Model via In-Context Reinforcement Learning
185. ICML 2024A General Framework for Sequential Decision-Making under Adaptivity Constraints
186. ICML 2024A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear MDPs
187. ICML 2024Accelerated Policy Gradient: On the Convergence Rates of the Nesterov Momentum for Reinforcement Learning
188. ICML 2024Acquiring Diverse Skills using Curriculum Reinforcement Learning with Mixture of Experts
189. ICML 2024Adaptive Advantage-Guided Policy Regularization for Offline Reinforcement Learning
190. ICML 2024Augmenting Decision with Hypothesis in Reinforcement Learning
191. ICML 2024Averaging $n$-step Returns Reduces Variance in Reinforcement Learning
192. ICML 2024BeigeMaps: Behavioral Eigenmaps for Reinforcement Learning from Images
193. ICML 2024Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short Delays
194. ICML 2024Breadth-First Exploration on Adaptive Grid for Reinforcement Learning
195. ICML 2024Confidence Aware Inverse Constrained Reinforcement Learning
196. ICML 2024Constrained Reinforcement Learning Under Model Mismatch
197. ICML 2024Coprocessor Actor Critic: A Model-Based Reinforcement Learning Approach For Adaptive Brain Stimulation
198. ICML 2024Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning
199. ICML 2024DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching
200. ICML 2024Discovering Multiple Solutions from a Single Task in Offline Reinforcement Learning

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。