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 72 篇，第 1 / 2 页

1. ICML 2023A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints
2. ICML 2023A Reinforcement Learning Framework for Dynamic Mediation Analysis
3. ICML 2023Adversarial Learning of Distributional Reinforcement Learning
4. ICML 2023An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning
5. ICML 2023Attention-Based Recurrence for Multi-Agent Reinforcement Learning under Stochastic Partial Observability
6. ICML 2023Automatic Intrinsic Reward Shaping for Exploration in Deep Reinforcement Learning
7. ICML 2023Beyond Exponentially Fast Mixing in Average-Reward Reinforcement Learning via Multi-Level Monte Carlo Actor-Critic
8. ICML 2023Boosting Offline Reinforcement Learning with Action Preference Query
9. ICML 2023Bootstrapped Representations in Reinforcement Learning
10. ICML 2023Constrained Decision Transformer for Offline Safe Reinforcement Learning
11. ICML 2023Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learning
12. ICML 2023Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards
13. ICML 2023Emergence of Adaptive Circadian Rhythms in Deep Reinforcement Learning
14. ICML 2023Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments
15. ICML 2023Explaining Reinforcement Learning with Shapley Values
16. ICML 2023Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement Learning
17. ICML 2023GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Models
18. ICML 2023Hierarchical Programmatic Reinforcement Learning via Learning to Compose Programs
19. ICML 2023Identifiability and Generalizability in Constrained Inverse Reinforcement Learning
20. ICML 2023Improved Regret for Efficient Online Reinforcement Learning with Linear Function Approximation
21. ICML 2023Interactive Object Placement with Reinforcement Learning
22. ICML 2023Internally Rewarded Reinforcement Learning
23. ICML 2023Inverse Reinforcement Learning without Reinforcement Learning
24. ICML 2023Jump-Start Reinforcement Learning
25. ICML 2023Language Instructed Reinforcement Learning for Human-AI Coordination
26. ICML 2023LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option Framework
27. ICML 2023Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels
28. ICML 2023Model-based Offline Reinforcement Learning with Count-based Conservatism
29. ICML 2023Model-based Reinforcement Learning with Scalable Composite Policy Gradient Estimators
30. ICML 2023Model-Bellman Inconsistency for Model-based Offline Reinforcement Learning
31. ICML 2023Model-Free Robust Average-Reward Reinforcement Learning
32. ICML 2023Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR
33. ICML 2023Near-optimal Conservative Exploration in Reinforcement Learning under Episode-wise Constraints
34. ICML 2023Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes
35. ICML 2023Non-stationary Reinforcement Learning under General Function Approximation
36. ICML 2023Offline Reinforcement Learning with Closed-Form Policy Improvement Operators
37. ICML 2023Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning
38. ICML 2023Parallel $Q$-Learning: Scaling Off-policy Reinforcement Learning under Massively Parallel Simulation
39. ICML 2023Performative Reinforcement Learning
40. ICML 2023Policy Regularization with Dataset Constraint for Offline Reinforcement Learning
41. ICML 2023Posterior Sampling for Deep Reinforcement Learning
42. ICML 2023Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons
43. ICML 2023Provable Reset-free Reinforcement Learning by No-Regret Reduction
44. ICML 2023Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources
45. ICML 2023Reachability-Aware Laplacian Representation in Reinforcement Learning
46. ICML 2023Reinforcement Learning Can Be More Efficient with Multiple Rewards
47. ICML 2023Reinforcement Learning from Passive Data via Latent Intentions
48. ICML 2023Reinforcement Learning in Low-rank MDPs with Density Features
49. ICML 2023Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space
50. ICML 2023Reinforcement Learning with History Dependent Dynamic Contexts

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。