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 1,676 篇，第 4 / 34 页

151. ICLR 2026Incentivizing Consistent, Effective and Scalable Reasoning Capability in Audio LLMs via Reasoning Process Rewards
152. ICLR 2026Information-based Value Iteration Networks for Decision Making Under Uncertainty
153. ICLR 2026Intention-Conditioned Flow Occupancy Models
154. ICLR 2026Inter-Agent Relative Representations for Multi-Agent Option Discovery
155. ICLR 2026Invert4TVG: A Temporal Video Grounding Framework with Inversion Tasks Preserving Action Understanding Ability
156. ICLR 2026Is Pure Exploitation Sufficient in Exogenous MDPs with Linear Function Approximation?
157. ICLR 2026Jackpot: Align Actor-Policy Distribution for scalable and stable RL for LLM
158. ICLR 2026KL-Regularized Reinforcement Learning is Designed to Mode Collapse
159. ICLR 2026Koopman-Assisted Trajectory Synthesis: A Data Augmentation Framework for Offline Imitation Learning
160. ICLR 2026Latent Wasserstein Adversarial Imitation Learning
161. ICLR 2026Learning Dynamics Feature Representation via Policy Attention for Dynamic Path Planning in Urban Road Networks
162. ICLR 2026Learning Efficient and Interpretable Multi-Agent Communication
163. ICLR 2026Learning from Algorithm Feedback: One-Shot SAT Solver Guidance with GNNs
164. ICLR 2026Learning Massively Multitask World Models for Continuous Control
165. ICLR 2026Learning More with Less: A Dynamic Dual-Level Down-Sampling Framework for Efficient Policy Optimization
166. ICLR 2026Learning to Be Uncertain: Pre-training World Models with Horizon-Calibrated Uncertainty
167. ICLR 2026Learning To Draft: Adaptive Speculative Decoding with Reinforcement Learning
168. ICLR 2026Learning to Reason as Action Abstractions with Scalable Mid-Training RL
169. ICLR 2026Learning to Reason Efficiently with Discounted Reinforcement Learning
170. ICLR 2026Less Is More: Clustered Cross-Covariance Control for Offline RL
171. ICLR 2026Leveraging Explanation to Improve Generalization of Meta Reinforcement Learning
172. ICLR 2026Lipschitz Bandits with Stochastic Delayed Feedback
173. ICLR 2026Local Reinforcement Learning with Action-Conditioned Root Mean Squared Q-Functions
174. ICLR 2026LongRLVR: Long-Context Reinforcement Learning Requires Verifiable Context Rewards
175. ICLR 2026Look Back to Reason Forward: Revisitable Memory for Long-Context LLM Agents
176. ICLR 2026Look-ahead Reasoning with a Learned Model in Imperfect Information Games
177. ICLR 2026MAGE: Multi-scale Autoregressive Generation for Offline Reinforcement Learning
178. ICLR 2026MAGO: Beyond Fixed Hyperparameters with Multi-Objective Pareto Optimization for Hybrid LLM Reasoning
179. ICLR 2026Masked Skill Token Training for Hierarchical Off-Dynamics Transfer
180. ICLR 2026Mastering Sparse CUDA Generation through Pretrained Models and Deep Reinforcement Learning
181. ICLR 2026Matching multiple experts: on the exploitability of multi-agent imitation learning
182. ICLR 2026Mean Flow Policy with Instantaneous Velocity Constraint for One-step Action Generation
183. ICLR 2026Memory-T1: Reinforcement Learning for Temporal Reasoning in Multi-session Agents
184. ICLR 2026Memory, Benchmark & Robots: A Benchmark for Solving Complex Tasks with Reinforcement Learning
185. ICLR 2026Minimax Optimal Adversarial Reinforcement Learning
186. ICLR 2026MIRA: Memory-Integrated Reinforcement Learning Agent with Limited LLM Guidance
187. ICLR 2026MIRACLE: Model-free Imitation and Reinforcement Learning for Adaptive Cut-Selection
188. ICLR 2026Mirage or Method? How Model–Task Alignment Induces Divergent RL Conclusions
189. ICLR 2026Mixture-of-World Models: Scaling Multi-Task Reinforcement Learning with Modular Latent Dynamics
190. ICLR 2026MOBODY: Model-Based Off-Dynamics Offline Reinforcement Learning
191. ICLR 2026Model-based Offline RL via Robust Value-Aware Model Learning with Implicitly Differentiable Adaptive Weighting
192. ICLR 2026Multi-Action Self-Improvement For Neural Combinatorial Optimization
193. ICLR 2026Multi-Agent Guided Policy Optimization
194. ICLR 2026MVR: Multi-view Video Reward Shaping for Reinforcement Learning
195. ICLR 2026Near-Optimal Sample Complexity Bounds for Constrained Average-Reward MDPs
196. ICLR 2026Near-Optimal Second-Order Guarantees for Model-Based Adversarial Imitation Learning
197. ICLR 2026Negotiated Reasoning: On Provably Addressing Relative Over-Generalization
198. ICLR 2026Neural+Symbolic Approaches for Interpretable Actor-Critic Reinforcement Learning
199. ICLR 2026Object-Centric World Models from Few-Shot Annotations for Sample-Efficient Reinforcement Learning
200. ICLR 2026Occupancy Reward Shaping: Improving Credit Assignment for Offline Goal-Conditioned 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。