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 8 / 9 页

351. NeurIPS 2024Maximum Entropy Reinforcement Learning via Energy-Based Normalizing Flow
352. NeurIPS 2024MetaCURL: Non-stationary Concave Utility Reinforcement Learning
353. NeurIPS 2024Mitigating Covariate Shift in Behavioral Cloning via Robust Stationary Distribution Correction
354. NeurIPS 2024Model-free Low-Rank Reinforcement Learning via Leveraged Entry-wise Matrix Estimation
355. NeurIPS 2024Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model
356. NeurIPS 2024Near-Optimal Distributionally Robust Reinforcement Learning with General $L_p$ Norms
357. NeurIPS 2024NeoRL: Efficient Exploration for Nonepisodic RL
358. NeurIPS 2024ODRL: A Benchmark for Off-Dynamics Reinforcement Learning
359. NeurIPS 2024Offline Multitask Representation Learning for Reinforcement Learning
360. NeurIPS 2024Offline Oracle-Efficient Learning for Contextual MDPs via Layerwise Exploration-Exploitation Tradeoff
361. NeurIPS 2024Optimizing Automatic Differentiation with Deep Reinforcement Learning
362. NeurIPS 2024Oracle-Efficient Reinforcement Learning for Max Value Ensembles
363. NeurIPS 2024Parallelizing Model-based Reinforcement Learning Over the Sequence Length
364. NeurIPS 2024Parseval Regularization for Continual Reinforcement Learning
365. NeurIPS 2024PEAC: Unsupervised Pre-training for Cross-Embodiment Reinforcement Learning
366. NeurIPS 2024Provable Partially Observable Reinforcement Learning with Privileged Information
367. NeurIPS 2024Provably and Practically Efficient Adversarial Imitation Learning with General Function Approximation
368. NeurIPS 2024Provably Efficient Reinforcement Learning with Multinomial Logit Function Approximation
369. NeurIPS 2024Q-Distribution guided Q-learning for offline reinforcement learning: Uncertainty penalized Q-value via consistency model
370. NeurIPS 2024Randomized Exploration for Reinforcement Learning with Multinomial Logistic Function Approximation
371. NeurIPS 2024REBEL: Reinforcement Learning via Regressing Relative Rewards
372. NeurIPS 2024Recurrent Reinforcement Learning with Memoroids
373. NeurIPS 2024Regularized Q-Learning
374. NeurIPS 2024Reinforcement Learning with Adaptive Regularization for Safe Control of Critical Systems
375. NeurIPS 2024Reinforcement Learning with Euclidean Data Augmentation for State-Based Continuous Control
376. NeurIPS 2024Reinforcement Learning with Lookahead Information
377. NeurIPS 2024Rethinking Exploration in Reinforcement Learning with Effective Metric-Based Exploration Bonus
378. NeurIPS 2024Rethinking Inverse Reinforcement Learning: from Data Alignment to Task Alignment
379. NeurIPS 2024Rethinking Optimal Transport in Offline Reinforcement Learning
380. NeurIPS 2024Robust Reinforcement Learning with General Utility
381. NeurIPS 2024Sample-Efficient Constrained Reinforcement Learning with General Parameterization
382. NeurIPS 2024Simplifying Constraint Inference with Inverse Reinforcement Learning
383. NeurIPS 2024Simplifying Latent Dynamics with Softly State-Invariant World Models
384. NeurIPS 2024Solving Minimum-Cost Reach Avoid using Reinforcement Learning
385. NeurIPS 2024Span-Based Optimal Sample Complexity for Weakly Communicating and General Average Reward MDPs
386. NeurIPS 2024Spectral-Risk Safe Reinforcement Learning with Convergence Guarantees
387. NeurIPS 2024SPRINQL: Sub-optimal Demonstrations driven Offline Imitation Learning
388. NeurIPS 2024State Chrono Representation for Enhancing Generalization in Reinforcement Learning
389. NeurIPS 2024State-free Reinforcement Learning
390. NeurIPS 2024Statistical Efficiency of Distributional Temporal Difference Learning
391. NeurIPS 2024Sub-optimal Experts mitigate Ambiguity in Inverse Reinforcement Learning
392. NeurIPS 2024Subwords as Skills: Tokenization for Sparse-Reward Reinforcement Learning
393. NeurIPS 2024Swift Sampler: Efficient Learning of Sampler by 10 Parameters
394. NeurIPS 2024Taming "data-hungry" reinforcement learning? Stability in continuous state-action spaces
395. NeurIPS 2024The Edge-of-Reach Problem in Offline Model-Based Reinforcement Learning
396. NeurIPS 2024The Surprising Ineffectiveness of Pre-Trained Visual Representations for Model-Based Reinforcement Learning
397. NeurIPS 2024The Value of Reward Lookahead in Reinforcement Learning
398. NeurIPS 2024Time-Constrained Robust MDPs
399. NeurIPS 2024Towards the Transferability of Rewards Recovered via Regularized Inverse Reinforcement Learning
400. NeurIPS 2024Unlock the Intermittent Control Ability of Model Free 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。