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 5 / 6 页

201. NeurIPS 2023Iterative Reachability Estimation for Safe Reinforcement Learning
202. NeurIPS 2023Keep Various Trajectories: Promoting Exploration of Ensemble Policies in Continuous Control
203. NeurIPS 2023Kernelized Reinforcement Learning with Order Optimal Regret Bounds
204. NeurIPS 2023KuaiSim: A Comprehensive Simulator for Recommender Systems
205. NeurIPS 2023Latent exploration for Reinforcement Learning
206. NeurIPS 2023Learning Better with Less: Effective Augmentation for Sample-Efficient Visual Reinforcement Learning
207. NeurIPS 2023Learning Generalizable Agents via Saliency-guided Features Decorrelation
208. NeurIPS 2023Learning Regularized Monotone Graphon Mean-Field Games
209. NeurIPS 2023Learning Score-based Grasping Primitive for Human-assisting Dexterous Grasping
210. NeurIPS 2023Learning to Influence Human Behavior with Offline Reinforcement Learning
211. NeurIPS 2023LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios
212. NeurIPS 2023Long-Term Fairness with Unknown Dynamics
213. NeurIPS 2023Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline RL
214. NeurIPS 2023Loss Dynamics of Temporal Difference Reinforcement Learning
215. NeurIPS 2023MIMEx: Intrinsic Rewards from Masked Input Modeling
216. NeurIPS 2023Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks
217. NeurIPS 2023Model-Free Active Exploration in Reinforcement Learning
218. NeurIPS 2023Model-free Posterior Sampling via Learning Rate Randomization
219. NeurIPS 2023Model-Free Reinforcement Learning with the Decision-Estimation Coefficient
220. NeurIPS 2023Monte Carlo Tree Search with Boltzmann Exploration
221. NeurIPS 2023Multi-Modal Inverse Constrained Reinforcement Learning from a Mixture of Demonstrations
222. NeurIPS 2023Mutual Information Regularized Offline Reinforcement Learning
223. NeurIPS 2023Natural Actor-Critic for Robust Reinforcement Learning with Function Approximation
224. NeurIPS 2023ODE-based Recurrent Model-free Reinforcement Learning for POMDPs
225. NeurIPS 2023OFCOURSE: A Multi-Agent Reinforcement Learning Environment for Order Fulfillment
226. NeurIPS 2023Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value Regularization
227. NeurIPS 2023Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management
228. NeurIPS 2023Offline Reinforcement Learning with Differential Privacy
229. NeurIPS 2023On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes
230. NeurIPS 2023On the Importance of Exploration for Generalization in Reinforcement Learning
231. NeurIPS 2023One Risk to Rule Them All: A Risk-Sensitive Perspective on Model-Based Offline Reinforcement Learning
232. NeurIPS 2023Optimal Convergence Rate for Exact Policy Mirror Descent in Discounted Markov Decision Processes
233. NeurIPS 2023Optimistic Active Exploration of Dynamical Systems
234. NeurIPS 2023Optimistic Exploration in Reinforcement Learning Using Symbolic Model Estimates
235. NeurIPS 2023Parameterizing Non-Parametric Meta-Reinforcement Learning Tasks via Subtask Decomposition
236. NeurIPS 2023Performance Bounds for Policy-Based Average Reward Reinforcement Learning Algorithms
237. NeurIPS 2023Pgx: Hardware-Accelerated Parallel Game Simulators for Reinforcement Learning
238. NeurIPS 2023PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control Tasks
239. NeurIPS 2023Pitfall of Optimism: Distributional Reinforcement Learning by Randomizing Risk Criterion
240. NeurIPS 2023Policy Finetuning in Reinforcement Learning via Design of Experiments using Offline Data
241. NeurIPS 2023Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation
242. NeurIPS 2023Prioritizing Samples in Reinforcement Learning with Reducible Loss
243. NeurIPS 2023Provably Efficient Offline Goal-Conditioned Reinforcement Learning with General Function Approximation and Single-Policy Concentrability
244. NeurIPS 2023Provably Efficient Offline Reinforcement Learning in Regular Decision Processes
245. NeurIPS 2023Provably Robust Temporal Difference Learning for Heavy-Tailed Rewards
246. NeurIPS 2023Provably Safe Reinforcement Learning with Step-wise Violation Constraints
247. NeurIPS 2023Recovering from Out-of-sample States via Inverse Dynamics in Offline Reinforcement Learning
248. NeurIPS 2023Reduced Policy Optimization for Continuous Control with Hard Constraints
249. NeurIPS 2023Reflexion: language agents with verbal reinforcement learning
250. NeurIPS 2023Regret Minimization via Saddle Point Optimization

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。