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 6 / 9 页

251. ICML 2024Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective
252. ICML 2024Langevin Policy for Safe Reinforcement Learning
253. ICML 2024Language Agents with Reinforcement Learning for Strategic Play in the Werewolf Game
254. ICML 2024Learning Coverage Paths in Unknown Environments with Deep Reinforcement Learning
255. ICML 2024Listwise Reward Estimation for Offline Preference-based Reinforcement Learning
256. ICML 2024LLM-Empowered State Representation for Reinforcement Learning
257. ICML 2024Meta-Reinforcement Learning Robust to Distributional Shift Via Performing Lifelong In-Context Learning
258. ICML 2024Model-based Reinforcement Learning for Confounded POMDPs
259. ICML 2024Model-based Reinforcement Learning for Parameterized Action Spaces
260. ICML 2024Momentum for the Win: Collaborative Federated Reinforcement Learning across Heterogeneous Environments
261. ICML 2024More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning
262. ICML 2024No-Regret Reinforcement Learning in Smooth MDPs
263. ICML 2024Offline Actor-Critic Reinforcement Learning Scales to Large Models
264. ICML 2024Pausing Policy Learning in Non-stationary Reinforcement Learning
265. ICML 2024Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning
266. ICML 2024PIPER: Primitive-Informed Preference-based Hierarchical Reinforcement Learning via Hindsight Relabeling
267. ICML 2024Position: Benchmarking is Limited in Reinforcement Learning Research
268. ICML 2024Position: Reinforcement Learning in Dynamic Treatment Regimes Needs Critical Reexamination
269. ICML 2024Probabilistic Subgoal Representations for Hierarchical Reinforcement Learning
270. ICML 2024Provable Representation with Efficient Planning for Partially Observable Reinforcement Learning
271. ICML 2024Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation
272. ICML 2024Provably Efficient Partially Observable Risk-sensitive Reinforcement Learning with Hindsight Observation
273. ICML 2024Q-value Regularized Transformer for Offline Reinforcement Learning
274. ICML 2024Random Latent Exploration for Deep Reinforcement Learning
275. ICML 2024Reinforcement Learning from Reachability Specifications: PAC Guarantees with Expected Conditional Distance
276. ICML 2024Rethinking Decision Transformer via Hierarchical Reinforcement Learning
277. ICML 2024Reward Shaping for Reinforcement Learning with An Assistant Reward Agent
278. ICML 2024Reward-Free Kernel-Based Reinforcement Learning
279. ICML 2024RICE: Breaking Through the Training Bottlenecks of Reinforcement Learning with Explanation
280. ICML 2024Rich-Observation Reinforcement Learning with Continuous Latent Dynamics
281. ICML 2024RIME: Robust Preference-based Reinforcement Learning with Noisy Preferences
282. ICML 2024Risk-Sensitive Reward-Free Reinforcement Learning with CVaR
283. ICML 2024Robust Inverse Constrained Reinforcement Learning under Model Misspecification
284. ICML 2024RVI-SAC: Average Reward Off-Policy Deep Reinforcement Learning
285. ICML 2024Safe Reinforcement Learning using Finite-Horizon Gradient-based Estimation
286. ICML 2024Sample-Efficient Multiagent Reinforcement Learning with Reset Replay
287. ICML 2024Sequence Compression Speeds Up Credit Assignment in Reinforcement Learning
288. ICML 2024SHINE: Shielding Backdoors in Deep Reinforcement Learning
289. ICML 2024Simple Ingredients for Offline Reinforcement Learning
290. ICML 2024Single-Trajectory Distributionally Robust Reinforcement Learning
291. ICML 2024SiT: Symmetry-invariant Transformers for Generalisation in Reinforcement Learning
292. ICML 2024Switching the Loss Reduces the Cost in Batch Reinforcement Learning
293. ICML 2024Temporal Logic Specification-Conditioned Decision Transformer for Offline Safe Reinforcement Learning
294. ICML 2024Test-Time Regret Minimization in Meta Reinforcement Learning
295. ICML 2024To the Max: Reinventing Reward in Reinforcement Learning
296. ICML 2024Towards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oracles
297. ICML 2024Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption
298. ICML 2024Uncertainty-Aware Reward-Free Exploration with General Function Approximation
299. ICML 2024Unlock the Cognitive Generalization of Deep Reinforcement Learning via Granular Ball Representation
300. ICML 2024Value-Evolutionary-Based 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。