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 1,676 篇，第 26 / 34 页

1251. ICML 2024Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning
1252. ICML 2024PIPER: Primitive-Informed Preference-based Hierarchical Reinforcement Learning via Hindsight Relabeling
1253. ICML 2024Position: Benchmarking is Limited in Reinforcement Learning Research
1254. ICML 2024Position: Reinforcement Learning in Dynamic Treatment Regimes Needs Critical Reexamination
1255. ICML 2024Probabilistic Subgoal Representations for Hierarchical Reinforcement Learning
1256. ICML 2024Provable Representation with Efficient Planning for Partially Observable Reinforcement Learning
1257. ICML 2024Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation
1258. ICML 2024Provably Efficient Partially Observable Risk-sensitive Reinforcement Learning with Hindsight Observation
1259. ICML 2024Q-value Regularized Transformer for Offline Reinforcement Learning
1260. ICML 2024Random Latent Exploration for Deep Reinforcement Learning
1261. ICML 2024Reinforcement Learning from Reachability Specifications: PAC Guarantees with Expected Conditional Distance
1262. ICML 2024Rethinking Decision Transformer via Hierarchical Reinforcement Learning
1263. ICML 2024Reward Shaping for Reinforcement Learning with An Assistant Reward Agent
1264. ICML 2024Reward-Free Kernel-Based Reinforcement Learning
1265. ICML 2024RICE: Breaking Through the Training Bottlenecks of Reinforcement Learning with Explanation
1266. ICML 2024Rich-Observation Reinforcement Learning with Continuous Latent Dynamics
1267. ICML 2024RIME: Robust Preference-based Reinforcement Learning with Noisy Preferences
1268. ICML 2024Risk-Sensitive Reward-Free Reinforcement Learning with CVaR
1269. ICML 2024Robust Inverse Constrained Reinforcement Learning under Model Misspecification
1270. ICML 2024RVI-SAC: Average Reward Off-Policy Deep Reinforcement Learning
1271. ICML 2024Safe Reinforcement Learning using Finite-Horizon Gradient-based Estimation
1272. ICML 2024Sample-Efficient Multiagent Reinforcement Learning with Reset Replay
1273. ICML 2024Sequence Compression Speeds Up Credit Assignment in Reinforcement Learning
1274. ICML 2024SHINE: Shielding Backdoors in Deep Reinforcement Learning
1275. ICML 2024Simple Ingredients for Offline Reinforcement Learning
1276. ICML 2024Single-Trajectory Distributionally Robust Reinforcement Learning
1277. ICML 2024SiT: Symmetry-invariant Transformers for Generalisation in Reinforcement Learning
1278. ICML 2024Switching the Loss Reduces the Cost in Batch Reinforcement Learning
1279. ICML 2024Temporal Logic Specification-Conditioned Decision Transformer for Offline Safe Reinforcement Learning
1280. ICML 2024Test-Time Regret Minimization in Meta Reinforcement Learning
1281. ICML 2024To the Max: Reinventing Reward in Reinforcement Learning
1282. ICML 2024Towards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oracles
1283. ICML 2024Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption
1284. ICML 2024Uncertainty-Aware Reward-Free Exploration with General Function Approximation
1285. ICML 2024Unlock the Cognitive Generalization of Deep Reinforcement Learning via Granular Ball Representation
1286. ICML 2024Value-Evolutionary-Based Reinforcement Learning
1287. ICML 2024When Do Skills Help Reinforcement Learning? A Theoretical Analysis of Temporal Abstractions
1288. ICML 2024Zero-Sum Positional Differential Games as a Framework for Robust Reinforcement Learning: Deep Q-Learning Approach
1289. NeurIPS 2024A Method for Evaluating Hyperparameter Sensitivity in Reinforcement Learning
1290. NeurIPS 2024A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation
1291. NeurIPS 2024A Study of Plasticity Loss in On-Policy Deep Reinforcement Learning
1292. NeurIPS 2024A theoretical case-study of Scalable Oversight in Hierarchical Reinforcement Learning
1293. NeurIPS 2024A Unified Principle of Pessimism for Offline Reinforcement Learning under Model Mismatch
1294. NeurIPS 2024A2PO: Towards Effective Offline Reinforcement Learning from an Advantage-aware Perspective
1295. NeurIPS 2024Achieving Constant Regret in Linear Markov Decision Processes
1296. NeurIPS 2024Achieving Tractable Minimax Optimal Regret in Average Reward MDPs
1297. NeurIPS 2024Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps
1298. NeurIPS 2024Adaptive Exploration for Data-Efficient General Value Function Evaluations
1299. NeurIPS 2024Adversarially Trained Weighted Actor-Critic for Safe Offline Reinforcement Learning
1300. NeurIPS 2024Beyond Optimism: Exploration With Partially Observable Rewards

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。