强化学习论文趋势 2023–2026

主要结论

• 强化学习论文从 2023 年的 1,070 篇增长到 2026 年的 2,985 篇，三年间扩大到约 2.8 倍。
• NeurIPS 是发强化学习论文最多的会议，2023–2026 年共 2,494 篇，占四个会议总量的 36%。
• 交叉方向里涨得最快的是推理模型：同时属于两个主题的论文从 2023 年的 9 篇增至 2026 年的 437 篇。

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 2026\texttt{FEROM}: Frontier Endogenous Reveal-Order Marginal Policy Optimization for Masked Diffusion LMs
• ICML 2026$\text{DT}^\text{2}$: Decision-Targeted Digital Twins
• ICLR 2026$\nabla$-Reasoner: LLM Reasoning via Test-Time Gradient Descent in Textual Space
• CVPR 2026A Causal Marriage between VLM and IRM from Understanding to
• NeurIPS 2025$\texttt{G1}$: Teaching LLMs to Reason on Graphs with Reinforcement Learning
• ICML 2025A Computationally Efficient Algorithm for Infinite-Horizon Average-Reward Linear MDPs
• ICLR 2025$\phi$-Update: A Class of Policy Update Methods with Policy Convergence Guarantee
• CVPR 2025All-Optical Nonlinear Diffractive Deep Network for Ultrafast Image Denoising
• NeurIPS 2024A Critical Evaluation of AI Feedback for Aligning Large Language Models
• ICML 2024${\rm E}(3)$-Equivariant Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning
• ICLR 2024A Black-box Approach for Non-stationary Multi-agent Reinforcement Learning
• CVPR 2024DMR: Decomposed Multi-Modality Representations for Frames and Events Fusion in Visual Reinforcement Learning
• NeurIPS 2023“Why Not Looking backward?” A Robust Two-Step Method to Automatically Terminate Bayesian Optimization
• ICML 2023A Connection between One-Step RL and Critic Regularization in Reinforcement Learning
• ICLR 2023$O(T^{-1})$ Convergence of Optimistic-Follow-the-Regularized-Leader in Two-Player Zero-Sum Markov Games
• CVPR 2023"Seeing" Electric Network Frequency From Events
• NeurIPS 2026$\varphi$TD: Distributional Reinforcement Learning using Characteristic Functions
• ICML 2026$\textit{S}$-SPPO: Semantic-Calibrated Self-Play Preference Optimization
• ICLR 2026$AutoDrive\text{-}P^3$: Unified Chain of Perception–Prediction–Planning Thought via Reinforcement Fine-Tuning
• CVPR 2026AdaptVision Efficient Vision-Language Models via Adaptive Visual Acquisition

全部强化学习论文

共 2,494 篇，第 47 / 50 页

2301. NeurIPS 2023OFCOURSE: A Multi-Agent Reinforcement Learning Environment for Order Fulfillment
2302. NeurIPS 2023Off-Policy Evaluation for Human Feedback
2303. NeurIPS 2023Offline Imitation Learning with Variational Counterfactual Reasoning
2304. NeurIPS 2023Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage
2305. NeurIPS 2023Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value Regularization
2306. NeurIPS 2023Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management
2307. NeurIPS 2023Offline Reinforcement Learning with Differential Privacy
2308. NeurIPS 2023Offline RL with Discrete Proxy Representations for Generalizability in POMDPs
2309. NeurIPS 2023On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes
2310. NeurIPS 2023On Imitation in Mean-field Games
2311. NeurIPS 2023On Sample-Efficient Offline Reinforcement Learning: Data Diversity, Posterior Sampling and Beyond
2312. NeurIPS 2023On the Convergence and Sample Complexity Analysis of Deep Q-Networks with $\epsilon$-Greedy Exploration
2313. NeurIPS 2023On the Importance of Exploration for Generalization in Reinforcement Learning
2314. NeurIPS 2023On the Properties of Kullback-Leibler Divergence Between Multivariate Gaussian Distributions
2315. NeurIPS 2023One Risk to Rule Them All: A Risk-Sensitive Perspective on Model-Based Offline Reinforcement Learning
2316. NeurIPS 2023OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling
2317. NeurIPS 2023Online (Multinomial) Logistic Bandit: Improved Regret and Constant Computation Cost
2318. NeurIPS 2023Online Adaptive Policy Selection in Time-Varying Systems: No-Regret via Contractive Perturbations
2319. NeurIPS 2023Online Nonstochastic Model-Free Reinforcement Learning
2320. NeurIPS 2023Online RL in Linearly $q^\pi$-Realizable MDPs Is as Easy as in Linear MDPs If You Learn What to Ignore
2321. NeurIPS 2023OpenAssistant Conversations - Democratizing Large Language Model Alignment
2322. NeurIPS 2023Optimal Convergence Rate for Exact Policy Mirror Descent in Discounted Markov Decision Processes
2323. NeurIPS 2023Optimal cross-learning for contextual bandits with unknown context distributions
2324. NeurIPS 2023Optimal Exploration for Model-Based RL in Nonlinear Systems
2325. NeurIPS 2023Optimal Treatment Allocation for Efficient Policy Evaluation in Sequential Decision Making
2326. NeurIPS 2023Optimistic Active Exploration of Dynamical Systems
2327. NeurIPS 2023Optimistic Exploration in Reinforcement Learning Using Symbolic Model Estimates
2328. NeurIPS 2023Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL
2329. NeurIPS 2023Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Method
2330. NeurIPS 2023Ordering-based Conditions for Global Convergence of Policy Gradient Methods
2331. NeurIPS 2023PAC Learning Linear Thresholds from Label Proportions
2332. NeurIPS 2023Parallel-mentoring for Offline Model-based Optimization
2333. NeurIPS 2023Parameterizing Non-Parametric Meta-Reinforcement Learning Tasks via Subtask Decomposition
2334. NeurIPS 2023Percentile Criterion Optimization in Offline Reinforcement Learning
2335. NeurIPS 2023Performance Bounds for Policy-Based Average Reward Reinforcement Learning Algorithms
2336. NeurIPS 2023Pgx: Hardware-Accelerated Parallel Game Simulators for Reinforcement Learning
2337. NeurIPS 2023PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control Tasks
2338. NeurIPS 2023Pitfall of Optimism: Distributional Reinforcement Learning by Randomizing Risk Criterion
2339. NeurIPS 2023PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement Learning
2340. NeurIPS 2023Policy Finetuning in Reinforcement Learning via Design of Experiments using Offline Data
2341. NeurIPS 2023Policy Gradient for Rectangular Robust Markov Decision Processes
2342. NeurIPS 2023Policy Optimization for Continuous Reinforcement Learning
2343. NeurIPS 2023Policy Optimization in a Noisy Neighborhood: On Return Landscapes in Continuous Control
2344. NeurIPS 2023Policy Space Diversity for Non-Transitive Games
2345. NeurIPS 2023Posterior Sampling for Competitive RL: Function Approximation and Partial Observation
2346. NeurIPS 2023Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation
2347. NeurIPS 2023Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learning
2348. NeurIPS 2023Prediction and Control in Continual Reinforcement Learning
2349. NeurIPS 2023Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision
2350. NeurIPS 2023Prioritizing Samples in Reinforcement Learning with Reducible Loss

常见问题

2023–2026 年四大会议一共有多少篇强化学习论文？

共 6,885 篇，按年份分别是 2023 年 1,070 篇、2024 年 1,190 篇、2025 年 1,640 篇、2026 年 2,985 篇。

NeurIPS 2026 有多少篇强化学习论文？

NeurIPS 2026 有 760 篇强化学习论文，上一届是 784 篇。

ICLR 2026 有多少篇强化学习论文？

ICLR 2026 有 911 篇强化学习论文，上一届是 379 篇。

ICML 2026 有多少篇强化学习论文？

ICML 2026 有 1,021 篇强化学习论文，上一届是 405 篇。

CVPR 2026 有多少篇强化学习论文？

CVPR 2026 有 293 篇强化学习论文，上一届是 72 篇。

哪个会议的强化学习论文最多？

NeurIPS，2023–2026 年共 2,494 篇。