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 82 篇，第 1 / 2 页

1. ICML 2024A General Framework for Sequential Decision-Making under Adaptivity Constraints
2. ICML 2024A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear MDPs
3. ICML 2024Accelerated Policy Gradient: On the Convergence Rates of the Nesterov Momentum for Reinforcement Learning
4. ICML 2024Acquiring Diverse Skills using Curriculum Reinforcement Learning with Mixture of Experts
5. ICML 2024Adaptive Advantage-Guided Policy Regularization for Offline Reinforcement Learning
6. ICML 2024Augmenting Decision with Hypothesis in Reinforcement Learning
7. ICML 2024Averaging $n$-step Returns Reduces Variance in Reinforcement Learning
8. ICML 2024BeigeMaps: Behavioral Eigenmaps for Reinforcement Learning from Images
9. ICML 2024Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short Delays
10. ICML 2024Breadth-First Exploration on Adaptive Grid for Reinforcement Learning
11. ICML 2024Confidence Aware Inverse Constrained Reinforcement Learning
12. ICML 2024Constrained Reinforcement Learning Under Model Mismatch
13. ICML 2024Coprocessor Actor Critic: A Model-Based Reinforcement Learning Approach For Adaptive Brain Stimulation
14. ICML 2024Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning
15. ICML 2024DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching
16. ICML 2024Discovering Multiple Solutions from a Single Task in Offline Reinforcement Learning
17. ICML 2024Efficient Exploration in Average-Reward Constrained Reinforcement Learning: Achieving Near-Optimal Regret With Posterior Sampling
18. ICML 2024Emergence of In-Context Reinforcement Learning from Noise Distillation
19. ICML 2024End-to-End Neuro-Symbolic Reinforcement Learning with Textual Explanations
20. ICML 2024Enhancing Value Function Estimation through First-Order State-Action Dynamics in Offline Reinforcement Learning
21. ICML 2024Feasibility Consistent Representation Learning for Safe Reinforcement Learning
22. ICML 2024Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices
23. ICML 2024Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problem
24. ICML 2024FuRL: Visual-Language Models as Fuzzy Rewards for Reinforcement Learning
25. ICML 2024Hybrid Inverse Reinforcement Learning
26. ICML 2024Hybrid Reinforcement Learning from Offline Observation Alone
27. ICML 2024Improving Generalization in Offline Reinforcement Learning via Adversarial Data Splitting
28. ICML 2024In value-based deep reinforcement learning, a pruned network is a good network
29. ICML 2024In-Context Reinforcement Learning for Variable Action Spaces
30. ICML 2024Information-Directed Pessimism for Offline Reinforcement Learning
31. ICML 2024Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective
32. ICML 2024Langevin Policy for Safe Reinforcement Learning
33. ICML 2024Language Agents with Reinforcement Learning for Strategic Play in the Werewolf Game
34. ICML 2024Learning Coverage Paths in Unknown Environments with Deep Reinforcement Learning
35. ICML 2024Listwise Reward Estimation for Offline Preference-based Reinforcement Learning
36. ICML 2024LLM-Empowered State Representation for Reinforcement Learning
37. ICML 2024Meta-Reinforcement Learning Robust to Distributional Shift Via Performing Lifelong In-Context Learning
38. ICML 2024Model-based Reinforcement Learning for Confounded POMDPs
39. ICML 2024Model-based Reinforcement Learning for Parameterized Action Spaces
40. ICML 2024Momentum for the Win: Collaborative Federated Reinforcement Learning across Heterogeneous Environments
41. ICML 2024More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning
42. ICML 2024No-Regret Reinforcement Learning in Smooth MDPs
43. ICML 2024Offline Actor-Critic Reinforcement Learning Scales to Large Models
44. ICML 2024Pausing Policy Learning in Non-stationary Reinforcement Learning
45. ICML 2024Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning
46. ICML 2024PIPER: Primitive-Informed Preference-based Hierarchical Reinforcement Learning via Hindsight Relabeling
47. ICML 2024Position: Benchmarking is Limited in Reinforcement Learning Research
48. ICML 2024Position: Reinforcement Learning in Dynamic Treatment Regimes Needs Critical Reexamination
49. ICML 2024Probabilistic Subgoal Representations for Hierarchical Reinforcement Learning
50. ICML 2024Provable Representation with Efficient Planning for Partially Observable 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。