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 338 篇，第 6 / 7 页

251. ICML 2024Sequence Compression Speeds Up Credit Assignment in Reinforcement Learning
252. ICML 2024SHINE: Shielding Backdoors in Deep Reinforcement Learning
253. ICML 2024Simple Ingredients for Offline Reinforcement Learning
254. ICML 2024Single-Trajectory Distributionally Robust Reinforcement Learning
255. ICML 2024SiT: Symmetry-invariant Transformers for Generalisation in Reinforcement Learning
256. ICML 2024Switching the Loss Reduces the Cost in Batch Reinforcement Learning
257. ICML 2024Temporal Logic Specification-Conditioned Decision Transformer for Offline Safe Reinforcement Learning
258. ICML 2024Test-Time Regret Minimization in Meta Reinforcement Learning
259. ICML 2024To the Max: Reinventing Reward in Reinforcement Learning
260. ICML 2024Towards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oracles
261. ICML 2024Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption
262. ICML 2024Uncertainty-Aware Reward-Free Exploration with General Function Approximation
263. ICML 2024Unlock the Cognitive Generalization of Deep Reinforcement Learning via Granular Ball Representation
264. ICML 2024Value-Evolutionary-Based Reinforcement Learning
265. ICML 2024When Do Skills Help Reinforcement Learning? A Theoretical Analysis of Temporal Abstractions
266. ICML 2024Zero-Sum Positional Differential Games as a Framework for Robust Reinforcement Learning: Deep Q-Learning Approach
267. ICML 2023A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints
268. ICML 2023A Reinforcement Learning Framework for Dynamic Mediation Analysis
269. ICML 2023Adversarial Learning of Distributional Reinforcement Learning
270. ICML 2023An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning
271. ICML 2023Attention-Based Recurrence for Multi-Agent Reinforcement Learning under Stochastic Partial Observability
272. ICML 2023Automatic Intrinsic Reward Shaping for Exploration in Deep Reinforcement Learning
273. ICML 2023Beyond Exponentially Fast Mixing in Average-Reward Reinforcement Learning via Multi-Level Monte Carlo Actor-Critic
274. ICML 2023Boosting Offline Reinforcement Learning with Action Preference Query
275. ICML 2023Bootstrapped Representations in Reinforcement Learning
276. ICML 2023Constrained Decision Transformer for Offline Safe Reinforcement Learning
277. ICML 2023Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learning
278. ICML 2023Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards
279. ICML 2023Emergence of Adaptive Circadian Rhythms in Deep Reinforcement Learning
280. ICML 2023Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments
281. ICML 2023Explaining Reinforcement Learning with Shapley Values
282. ICML 2023Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement Learning
283. ICML 2023GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Models
284. ICML 2023Hierarchical Programmatic Reinforcement Learning via Learning to Compose Programs
285. ICML 2023Identifiability and Generalizability in Constrained Inverse Reinforcement Learning
286. ICML 2023Improved Regret for Efficient Online Reinforcement Learning with Linear Function Approximation
287. ICML 2023Interactive Object Placement with Reinforcement Learning
288. ICML 2023Internally Rewarded Reinforcement Learning
289. ICML 2023Inverse Reinforcement Learning without Reinforcement Learning
290. ICML 2023Jump-Start Reinforcement Learning
291. ICML 2023Language Instructed Reinforcement Learning for Human-AI Coordination
292. ICML 2023LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option Framework
293. ICML 2023Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels
294. ICML 2023Model-based Offline Reinforcement Learning with Count-based Conservatism
295. ICML 2023Model-based Reinforcement Learning with Scalable Composite Policy Gradient Estimators
296. ICML 2023Model-Bellman Inconsistency for Model-based Offline Reinforcement Learning
297. ICML 2023Model-Free Robust Average-Reward Reinforcement Learning
298. ICML 2023Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR
299. ICML 2023Near-optimal Conservative Exploration in Reinforcement Learning under Episode-wise Constraints
300. ICML 2023Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes

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。