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 468 篇，第 5 / 10 页

201. NeurIPS 2025Stable Gradients for Stable Learning at Scale in Deep Reinforcement Learning
202. NeurIPS 2025Staggered Environment Resets Improve Massively Parallel On-Policy Reinforcement Learning
203. NeurIPS 2025STAIR: Addressing Stage Misalignment through Temporal-Aligned Preference Reinforcement Learning
204. NeurIPS 2025STAR: Efficient Preference-based Reinforcement Learning via Dual Regularization
205. NeurIPS 2025State Entropy Regularization for Robust Reinforcement Learning
206. NeurIPS 2025Structured Reinforcement Learning for Combinatorial Decision-Making
207. NeurIPS 2025Tackling Continual Offline RL through Selective Weights Activation on Aligned Spaces
208. NeurIPS 2025The World Is Bigger: A Computationally-Embedded Perspective on the Big World Hypothesis
209. NeurIPS 2025Time Reversal Symmetry for Efficient Robotic Manipulations in Deep Reinforcement Learning
210. NeurIPS 2025To Think or Not To Think: A Study of Thinking in Rule-Based Visual Reinforcement Fine-Tuning
211. NeurIPS 2025Towards Large-Scale In-Context Reinforcement Learning by Meta-Training in Randomized Worlds
212. NeurIPS 2025Towards Provable Emergence of In-Context Reinforcement Learning
213. NeurIPS 2025Trajectory Graph Learning: Aligning with Long Trajectories in Reinforcement Learning Without Reward Design
214. NeurIPS 2025TTRL: Test-Time Reinforcement Learning
215. NeurIPS 2025Uncertainty-Sensitive Privileged Learning
216. NeurIPS 2025Unified Multimodal Chain-of-Thought Reward Model through Reinforcement Fine-Tuning
217. NeurIPS 2025Value Diffusion Reinforcement Learning
218. NeurIPS 2025Value Improved Actor Critic Algorithms
219. NeurIPS 2025Videos are Sample-Efficient Supervisions: Behavior Cloning from Videos via Latent Representations
220. NeurIPS 2025WHAT MAKES MATH PROBLEMS HARD FOR REINFORCEMENT LEARNING: A CASE STUDY
221. NeurIPS 2025When Can Model-Free Reinforcement Learning be Enough for Thinking?
222. NeurIPS 2024A Method for Evaluating Hyperparameter Sensitivity in Reinforcement Learning
223. NeurIPS 2024A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation
224. NeurIPS 2024A Study of Plasticity Loss in On-Policy Deep Reinforcement Learning
225. NeurIPS 2024A theoretical case-study of Scalable Oversight in Hierarchical Reinforcement Learning
226. NeurIPS 2024A Unified Principle of Pessimism for Offline Reinforcement Learning under Model Mismatch
227. NeurIPS 2024A2PO: Towards Effective Offline Reinforcement Learning from an Advantage-aware Perspective
228. NeurIPS 2024Achieving Constant Regret in Linear Markov Decision Processes
229. NeurIPS 2024Achieving Tractable Minimax Optimal Regret in Average Reward MDPs
230. NeurIPS 2024Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps
231. NeurIPS 2024Adaptive Exploration for Data-Efficient General Value Function Evaluations
232. NeurIPS 2024Adversarially Trained Weighted Actor-Critic for Safe Offline Reinforcement Learning
233. NeurIPS 2024Beyond Optimism: Exploration With Partially Observable Rewards
234. NeurIPS 2024CALE: Continuous Arcade Learning Environment
235. NeurIPS 2024Compositional Automata Embeddings for Goal-Conditioned Reinforcement Learning
236. NeurIPS 2024Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning
237. NeurIPS 2024Diffusion Imitation from Observation
238. NeurIPS 2024Diffusion Policies Creating a Trust Region for Offline Reinforcement Learning
239. NeurIPS 2024Diffusion-based Curriculum Reinforcement Learning
240. NeurIPS 2024Diffusion-DICE: In-Sample Diffusion Guidance for Offline Reinforcement Learning
241. NeurIPS 2024Distributional Reinforcement Learning with Regularized Wasserstein Loss
242. NeurIPS 2024Doubly Mild Generalization for Offline Reinforcement Learning
243. NeurIPS 2024Dual Critic Reinforcement Learning under Partial Observability
244. NeurIPS 2024Dynamic Model Predictive Shielding for Provably Safe Reinforcement Learning
245. NeurIPS 2024DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning
246. NeurIPS 2024Efficient Reinforcement Learning by Discovering Neural Pathways
247. NeurIPS 2024Enhancing Robustness in Deep Reinforcement Learning: A Lyapunov Exponent Approach
248. NeurIPS 2024Exclusively Penalized Q-learning for Offline Reinforcement Learning
249. NeurIPS 2024Exploiting the Replay Memory Before Exploring the Environment: Enhancing Reinforcement Learning Through Empirical MDP Iteration
250. NeurIPS 2024Exploring the Edges of Latent State Clusters for 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。