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 403 篇，第 5 / 9 页

201. ICLR 2024Towards Imitation Learning to Branch for MIP: A Hybrid Reinforcement Learning based Sample Augmentation Approach
202. ICLR 2024Towards Offline Opponent Modeling with In-context Learning
203. ICLR 2024Towards Optimal Regret in Adversarial Linear MDPs with Bandit Feedback
204. ICLR 2024Towards Principled Representation Learning from Videos for Reinforcement Learning
205. ICLR 2024Towards Robust Offline Reinforcement Learning under Diverse Data Corruption
206. ICLR 2024Tree Search-Based Policy Optimization under Stochastic Execution Delay
207. ICLR 2024True Knowledge Comes from Practice: Aligning Large Language Models with Embodied Environments via Reinforcement Learning
208. ICLR 2024Uncertainty-aware Constraint Inference in Inverse Constrained Reinforcement Learning
209. ICLR 2024Understanding when Dynamics-Invariant Data Augmentations Benefit Model-free Reinforcement Learning Updates
210. ICLR 2024Uni-O4: Unifying Online and Offline Deep Reinforcement Learning with Multi-Step On-Policy Optimization
211. ICLR 2024Uni-RLHF: Universal Platform and Benchmark Suite for Reinforcement Learning with Diverse Human Feedback
212. ICLR 2024Unified Projection-Free Algorithms for Adversarial DR-Submodular Optimization
213. ICLR 2024Unleashing the Power of Pre-trained Language Models for Offline Reinforcement Learning
214. ICLR 2024Unlocking the Power of Representations in Long-term Novelty-based Exploration
215. ICLR 2024Unveiling Options with Neural Network Decomposition
216. ICLR 2024Vanishing Gradients in Reinforcement Finetuning of Language Models
217. ICLR 2024Variance-aware Regret Bounds for Stochastic Contextual Dueling Bandits
218. ICLR 2024Vision-Language Models are Zero-Shot Reward Models for Reinforcement Learning
219. ICLR 2024When should we prefer Decision Transformers for Offline Reinforcement Learning?
220. ICLR 2024Whittle Index with Multiple Actions and State Constraint for Inventory Management
221. ICML 2024A General Framework for Sequential Decision-Making under Adaptivity Constraints
222. ICML 2024A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear MDPs
223. ICML 2024Accelerated Policy Gradient: On the Convergence Rates of the Nesterov Momentum for Reinforcement Learning
224. ICML 2024Acquiring Diverse Skills using Curriculum Reinforcement Learning with Mixture of Experts
225. ICML 2024Adaptive Advantage-Guided Policy Regularization for Offline Reinforcement Learning
226. ICML 2024Augmenting Decision with Hypothesis in Reinforcement Learning
227. ICML 2024Averaging $n$-step Returns Reduces Variance in Reinforcement Learning
228. ICML 2024BeigeMaps: Behavioral Eigenmaps for Reinforcement Learning from Images
229. ICML 2024Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short Delays
230. ICML 2024Breadth-First Exploration on Adaptive Grid for Reinforcement Learning
231. ICML 2024Confidence Aware Inverse Constrained Reinforcement Learning
232. ICML 2024Constrained Reinforcement Learning Under Model Mismatch
233. ICML 2024Coprocessor Actor Critic: A Model-Based Reinforcement Learning Approach For Adaptive Brain Stimulation
234. ICML 2024Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning
235. ICML 2024DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching
236. ICML 2024Discovering Multiple Solutions from a Single Task in Offline Reinforcement Learning
237. ICML 2024Efficient Exploration in Average-Reward Constrained Reinforcement Learning: Achieving Near-Optimal Regret With Posterior Sampling
238. ICML 2024Emergence of In-Context Reinforcement Learning from Noise Distillation
239. ICML 2024End-to-End Neuro-Symbolic Reinforcement Learning with Textual Explanations
240. ICML 2024Enhancing Value Function Estimation through First-Order State-Action Dynamics in Offline Reinforcement Learning
241. ICML 2024Feasibility Consistent Representation Learning for Safe Reinforcement Learning
242. ICML 2024Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices
243. ICML 2024Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problem
244. ICML 2024FuRL: Visual-Language Models as Fuzzy Rewards for Reinforcement Learning
245. ICML 2024Hybrid Inverse Reinforcement Learning
246. ICML 2024Hybrid Reinforcement Learning from Offline Observation Alone
247. ICML 2024Improving Generalization in Offline Reinforcement Learning via Adversarial Data Splitting
248. ICML 2024In value-based deep reinforcement learning, a pruned network is a good network
249. ICML 2024In-Context Reinforcement Learning for Variable Action Spaces
250. ICML 2024Information-Directed Pessimism for 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。