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 411 篇，第 7 / 9 页

301. ICML 2025Universal Approximation Theorem of Deep Q-Networks
302. ICML 2025Unveiling Markov heads in Pretrained Language Models for Offline Reinforcement Learning
303. ICML 2025Vintix: Action Model via In-Context Reinforcement Learning
304. NeurIPS 2025A Bayesian Fast-Slow Framework to Mitigate Interference in Non-Stationary Reinforcement Learning
305. NeurIPS 2025A Clean Slate for Offline Reinforcement Learning
306. NeurIPS 2025A Novel General Framework for Sharp Lower Bounds in Succinct Stochastic Bandits
307. NeurIPS 2025A Provable Approach for End-to-End Safe Reinforcement Learning
308. NeurIPS 2025A Reinforcement Learning-based Bidding Strategy for Data Consumers in Auction-based Federated Learning
309. NeurIPS 2025Actor-Free Continuous Control via Structurally Maximizable Q-Functions
310. NeurIPS 2025Adaptive Batch-Wise Sample Scheduling for Direct Preference Optimization
311. NeurIPS 2025Adaptive Neighborhood-Constrained Q Learning for Offline Reinforcement Learning
312. NeurIPS 2025Adaptive Surrogate Gradients for Sequential Reinforcement Learning in Spiking Neural Networks
313. NeurIPS 2025Approximating Shapley Explanations in Reinforcement Learning
314. NeurIPS 2025Automaton Constrained Q-Learning
315. NeurIPS 2025Behavior Injection: Preparing Language Models for Reinforcement Learning
316. NeurIPS 2025Beyond Average Value Function in Precision Medicine: Maximum Probability-Driven Reinforcement Learning for Survival Analysis
317. NeurIPS 2025Beyond Scalar Rewards: An Axiomatic Framework for Lexicographic MDPs
318. NeurIPS 2025Boundary-to-Region Supervision for Offline Safe Reinforcement Learning
319. NeurIPS 2025BraVE: Offline Reinforcement Learning for Discrete Combinatorial Action Spaces
320. NeurIPS 2025Breaking the Performance Ceiling in Reinforcement Learning requires Inference Strategies
321. NeurIPS 2025Capturing Individual Human Preferences with Reward Features
322. NeurIPS 2025Certifying Stability of Reinforcement Learning Policies using Generalized Lyapunov Functions
323. NeurIPS 2025COLA: Towards Efficient Multi-Objective Reinforcement Learning with Conflict Objective Regularization in Latent Space
324. NeurIPS 2025Compiler-R1: Towards Agentic Compiler Auto-tuning with Reinforcement Learning
325. NeurIPS 2025Composite Flow Matching for Reinforcement Learning with Shifted-Dynamics Data
326. NeurIPS 2025Computational Hardness of Reinforcement Learning with Partial $q^{\pi}$-Realizability
327. NeurIPS 2025DAIL: Beyond Task Ambiguity for Language-Conditioned Reinforcement Learning
328. NeurIPS 2025DAPO: An Open-Source LLM Reinforcement Learning System at Scale
329. NeurIPS 2025DISCOVER: Automated Curricula for Sparse-Reward Reinforcement Learning
330. NeurIPS 2025Doubly Robust Alignment for Large Language Models
331. NeurIPS 2025Dynamic Configuration for Cutting Plane Separators via Reinforcement Learning on Incremental Graph
332. NeurIPS 2025Efficient Preference-Based Reinforcement Learning: Randomized Exploration meets Experimental Design
333. NeurIPS 2025Eluder dimension: localise it!
334. NeurIPS 2025Enhancing Interpretability in Deep Reinforcement Learning through Semantic Clustering
335. NeurIPS 2025Explainably Safe Reinforcement Learning
336. NeurIPS 2025FANS: A Flatness-Aware Network Structure for Generalization in Offline Reinforcement Learning
337. NeurIPS 2025Finite Sample Analysis of Linear Temporal Difference Learning with Arbitrary Features
338. NeurIPS 2025From Experts to a Generalist: Toward General Whole-Body Control for Humanoid Robots
339. NeurIPS 2025Fuz-RL: A Fuzzy-Guided Robust Framework for Safe Reinforcement Learning under Uncertainty
340. NeurIPS 2025General-Reasoner: Advancing LLM Reasoning Across All Domains
341. NeurIPS 2025Global Convergence for Average Reward Constrained MDPs with Primal-Dual Actor Critic Algorithm
342. NeurIPS 2025Greedy Sampling Is Provably Efficient For RLHF
343. NeurIPS 2025Gymnasium: A Standard Interface for Reinforcement Learning Environments
344. NeurIPS 2025Horizon Reduction Makes RL Scalable
345. NeurIPS 2025How Ensembles of Distilled Policies Improve Generalisation in Reinforcement Learning
346. NeurIPS 2025Imagined Autocurricula
347. NeurIPS 2025Imitation Beyond Expectation Using Pluralistic Stochastic Dominance
348. NeurIPS 2025Imitation Learning with Temporal Logic Constraints
349. NeurIPS 2025Improving Model-Based Reinforcement Learning by Converging to Flatter Minima
350. NeurIPS 2025Improving Reward Models with Proximal Policy Exploration for Preference-Based 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。