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 71 篇，第 2 / 2 页

51. ICML 2025Position: Lifetime tuning is incompatible with continual reinforcement learning
52. ICML 2025Provable Maximum Entropy Manifold Exploration via Diffusion Models
53. ICML 2025Provably Efficient Exploration in Inverse Constrained Reinforcement Learning
54. ICML 2025QPRL : Learning Optimal Policies with Quasi-Potential Functions for Asymmetric Traversal
55. ICML 2025Regret-Free Reinforcement Learning for Temporal Logic Specifications
56. ICML 2025Reinforcement Learning with Random Time Horizons
57. ICML 2025Reinforcement Learning with Segment Feedback
58. ICML 2025Reward Translation via Reward Machine in Semi-Alignable MDPs
59. ICML 2025Reward-Guided Prompt Evolving in Reinforcement Learning for LLMs
60. ICML 2025Robust Offline Reinforcement Learning with Linearly Structured $f$-Divergence Regularization
61. ICML 2025RobustZero: Enhancing MuZero Reinforcement Learning Robustness to State Perturbations
62. ICML 2025Sleeping Reinforcement Learning
63. ICML 2025Stay Hungry, Keep Learning: Sustainable Plasticity for Deep Reinforcement Learning
64. ICML 2025Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning
65. ICML 2025Task-Aware Virtual Training: Enhancing Generalization in Meta-Reinforcement Learning for Out-of-Distribution Tasks
66. ICML 2025The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning
67. ICML 2025The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks
68. ICML 2025The Number of Trials Matters in Infinite-Horizon General-Utility Markov Decision Processes
69. ICML 2025Universal Approximation Theorem of Deep Q-Networks
70. ICML 2025Unveiling Markov heads in Pretrained Language Models for Offline Reinforcement Learning
71. ICML 2025Vintix: Action Model via In-Context 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。