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 113 篇，第 1 / 3 页

1. ICML 2026Action Manifold Smoothing: A Lipschitz Pathway Perspective on High-Dimensional Reinforcement Learning
2. ICML 2026Active Curriculum Refinement for Reinforcement Learning
3. ICML 2026ACTIVE-o3 : Empowering MLLMs with Active Perception via Pure Reinforcement Learning
4. ICML 2026AdamO: A Collapse-Suppressed Optimizer for Offline RL
5. ICML 2026Adaptive Reinforcement Learning for Unobservable Random Delays
6. ICML 2026AReaL-DTA: Dynamic Tree Attention for Efficient Reinforcement Learning of Large Language Models
7. ICML 2026ARLArena: A Unified Framework for Stable Agentic Reinforcement Learning
8. ICML 2026Beyond Token-level Supervision: Unlocking the Potential of Decoding-based Regression via Reinforcement Learning
9. ICML 2026BOOSTAPR: Boosting Automated Program Repair via Execution-Grounded Reinforcement Learning with Dual Reward Models
10. ICML 2026Bottleneck-Guided Spectral Subgoals For Offline Goal-Conditioned RL
11. ICML 2026BubbleSpec: Turning Long-Tail Bubbles into Speculative Rollout Drafts for Synchronous Reinforcement Learning
12. ICML 2026Chunk-Guided Q-Learning
13. ICML 2026Commit to the Bit: Reactive Reinforcement Learning Done Right
14. ICML 2026Compositional Behavioral Semantics for State Abstraction in Reinforcement Learning
15. ICML 2026Compositional Transduction with Latent Analogies for Offline Goal-Conditioned Reinforcement Learning
16. ICML 2026Contextual Rollout Bandits for Reinforcement Learning with Verifiable Rewards
17. ICML 2026Counterfactual Bootstrap for Robust Meta-Reinforcement Learning
18. ICML 2026Coupled Variational Reinforcement Learning for Language Model General Reasoning
19. ICML 2026Covariance Volume Maximization for Embodied Latent Exploration in Deep Reinforcement Learning
20. ICML 2026CSPO: Constraint-Sensitive Policy Optimization for Safe Reinforcement Learning
21. ICML 2026D-ARL: A Distribution-Matched Asynchronous Reinforcement Learning Framework for Language Reasoning
22. ICML 2026De-attribute to Forget for LLM Unlearning
23. ICML 2026Discounted Beta–Bernoulli Reward Estimation for Sample-Efficient Reinforcement Learning with Verifiable Rewards
24. ICML 2026Distributional Active Inference
25. ICML 2026Distributional Inverse Reinforcement Learning
26. ICML 2026DualCOIL: Offline Imitation Learning from Contrasting Demonstrations
27. ICML 2026Dynamic Optimizations of LLM Ensembles with Two-Stage Reinforcement Learning Agents
28. ICML 2026EchoRL: Reinforcement Learning via Rollout Echoing
29. ICML 2026Emergence of Exploration in Policy Gradient Reinforcement Learning via Retrying
30. ICML 2026Evaluating Parameter Efficient Methods for RLVR
31. ICML 2026Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning
32. ICML 2026Evolution Strategies at the Hyperscale
33. ICML 2026Exact Unlearning in Reinforcement Learning
34. ICML 2026Experience is the Best Teacher: Motivating Effective Exploration in Reinforcement Learning for LLMs
35. ICML 2026Formalizing Learning from Language Feedback with Provable Guarantees
36. ICML 2026Fox in the Henhouse: Supply-Chain Backdoor Attacks Against Reinforcement Learning
37. ICML 2026Frictional Q-Learning
38. ICML 2026From Absolute to Relative: Rethinking Reward Shaping in Group-Based Reinforcement Learning
39. ICML 2026From Reward-Free Representations to Preferences: Rethinking Offline Preference-Based Reinforcement Learning
40. ICML 2026Geometric Control of Out-of-Distribution Shift in Safe Offline RL
41. ICML 2026Good SFT Optimizes for SFT, Better SFT Prepares for Reinforcement Learning
42. ICML 2026ICR-RL: Deep Reinforcement Learning via In-Context-Regression
43. ICML 2026Impact of Connectivity on Laplacian Representations in Reinforcement Learning
44. ICML 2026Instance-Dependent Continuous-Time Reinforcement Learning via Maximum Likelihood Estimation
45. ICML 2026Intentional Updates for Streaming Reinforcement Learning
46. ICML 2026KAGE-Bench: Fast Known-Axis Visual Generalization Evaluation for Reinforcement Learning
47. ICML 2026Knapsack RL: Compute-Efficient Reinforcement Learning via Heterogeneous Rollout Allocation
48. ICML 2026Latent Representation Alignment for Offline Goal-Conditioned Reinforcement Learning
49. ICML 2026Learning Anisotropic Value Geometry with Finsler Reinforcement Learning
50. ICML 2026Learning Interpretable Options by Identifying Reward Diffusion Bottlenecks in 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。