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 7 / 9 页

301. ICML 2024When Do Skills Help Reinforcement Learning? A Theoretical Analysis of Temporal Abstractions
302. ICML 2024Zero-Sum Positional Differential Games as a Framework for Robust Reinforcement Learning: Deep Q-Learning Approach
303. NeurIPS 2024A Method for Evaluating Hyperparameter Sensitivity in Reinforcement Learning
304. NeurIPS 2024A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation
305. NeurIPS 2024A Study of Plasticity Loss in On-Policy Deep Reinforcement Learning
306. NeurIPS 2024A theoretical case-study of Scalable Oversight in Hierarchical Reinforcement Learning
307. NeurIPS 2024A Unified Principle of Pessimism for Offline Reinforcement Learning under Model Mismatch
308. NeurIPS 2024A2PO: Towards Effective Offline Reinforcement Learning from an Advantage-aware Perspective
309. NeurIPS 2024Achieving Constant Regret in Linear Markov Decision Processes
310. NeurIPS 2024Achieving Tractable Minimax Optimal Regret in Average Reward MDPs
311. NeurIPS 2024Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps
312. NeurIPS 2024Adaptive Exploration for Data-Efficient General Value Function Evaluations
313. NeurIPS 2024Adversarially Trained Weighted Actor-Critic for Safe Offline Reinforcement Learning
314. NeurIPS 2024Beyond Optimism: Exploration With Partially Observable Rewards
315. NeurIPS 2024CALE: Continuous Arcade Learning Environment
316. NeurIPS 2024Compositional Automata Embeddings for Goal-Conditioned Reinforcement Learning
317. NeurIPS 2024Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning
318. NeurIPS 2024Diffusion Imitation from Observation
319. NeurIPS 2024Diffusion Policies Creating a Trust Region for Offline Reinforcement Learning
320. NeurIPS 2024Diffusion-based Curriculum Reinforcement Learning
321. NeurIPS 2024Diffusion-DICE: In-Sample Diffusion Guidance for Offline Reinforcement Learning
322. NeurIPS 2024Distributional Reinforcement Learning with Regularized Wasserstein Loss
323. NeurIPS 2024Doubly Mild Generalization for Offline Reinforcement Learning
324. NeurIPS 2024Dual Critic Reinforcement Learning under Partial Observability
325. NeurIPS 2024Dynamic Model Predictive Shielding for Provably Safe Reinforcement Learning
326. NeurIPS 2024DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning
327. NeurIPS 2024Efficient Reinforcement Learning by Discovering Neural Pathways
328. NeurIPS 2024Enhancing Robustness in Deep Reinforcement Learning: A Lyapunov Exponent Approach
329. NeurIPS 2024Exclusively Penalized Q-learning for Offline Reinforcement Learning
330. NeurIPS 2024Exploiting the Replay Memory Before Exploring the Environment: Enhancing Reinforcement Learning Through Empirical MDP Iteration
331. NeurIPS 2024Exploring the Edges of Latent State Clusters for Goal-Conditioned Reinforcement Learning
332. NeurIPS 2024Federated Ensemble-Directed Offline Reinforcement Learning
333. NeurIPS 2024First-Explore, then Exploit: Meta-Learning to Solve Hard Exploration-Exploitation Trade-Offs
334. NeurIPS 2024Foundations of Multivariate Distributional Reinforcement Learning
335. NeurIPS 2024Goal Conditioned Reinforcement Learning for Photo Finishing Tuning
336. NeurIPS 2024Goal-Conditioned On-Policy Reinforcement Learning
337. NeurIPS 2024How does Inverse RL Scale to Large State Spaces? A Provably Efficient Approach
338. NeurIPS 2024How to Solve Contextual Goal-Oriented Problems with Offline Datasets?
339. NeurIPS 2024Hybrid Reinforcement Learning Breaks Sample Size Barriers In Linear MDPs
340. NeurIPS 2024Identifying Latent State-Transition Processes for Individualized Reinforcement Learning
341. NeurIPS 2024Implicit Curriculum in Procgen Made Explicit
342. NeurIPS 2024In-Trajectory Inverse Reinforcement Learning: Learn Incrementally Before an Ongoing Trajectory Terminates
343. NeurIPS 2024Is Value Learning Really the Main Bottleneck in Offline RL?
344. NeurIPS 2024Iteratively Refined Behavior Regularization for Offline Reinforcement Learning
345. NeurIPS 2024KALM: Knowledgeable Agents by Offline Reinforcement Learning from Large Language Model Rollouts
346. NeurIPS 2024Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret Algorithm
347. NeurIPS 2024Last-Iterate Global Convergence of Policy Gradients for Constrained Reinforcement Learning
348. NeurIPS 2024Learning General Parameterized Policies for Infinite Horizon Average Reward Constrained MDPs via Primal-Dual Policy Gradient Algorithm
349. NeurIPS 2024Learning Goal-Conditioned Representations for Language Reward Models
350. NeurIPS 2024Local Linearity: the Key for No-regret Reinforcement Learning in Continuous MDPs

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。