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 4 / 50 页

151. NeurIPS 2026Decentralized Multi-Goal Multi-Agent Pathfinding with Spatial Prior and Neighbor Intent Prediction
152. NeurIPS 2026Decision Focused Scenario Learning for Contextual Stochastic Programming
153. NeurIPS 2026Decision-Focused Learning in MDPs: An Occupancy Measure Approach
154. NeurIPS 2026Decomposed Graded Verifier for Generative World Modeling
155. NeurIPS 2026Decomposing the modulation of interactions between neuronal populations
156. NeurIPS 2026Decoupling Conversational Dynamics in Full-Duplex Spoken Models through Reinforcement Learning
157. NeurIPS 2026Decoupling Exploration and Policy Optimization: Uncertainty Guided Tree Search for Hard Exploration
158. NeurIPS 2026Deep Double Q-learning
159. NeurIPS 2026Deep Heteroskedastic Regression: Post-Hoc Variance Estimation from Latent Representations
160. NeurIPS 2026DeFlow: Decoupling Behavior-Prior Modeling and Value Maximization for Offline Policy Extraction
161. NeurIPS 2026DeFlowCritic: Dense Latent Reward Alignment for Text-to-Image Flow Matching Models
162. NeurIPS 2026Delightful Distributed Policy Gradient
163. NeurIPS 2026Democratizing Tool Learning with Environments Fully Simulated by a Free 8B Language Model
164. NeurIPS 2026Designing Reinforcement Learning for Diffusion Models: A Unified Path-Space View
165. NeurIPS 2026Dexterous Skill Discovery via Topology-Aware Wasserstein Dependency
166. NeurIPS 2026DGRAF: Observation-Quality-Aware Reinforcement Learning for Dynamic Reconfigurable Batteries
167. NeurIPS 2026Differentiable Belief-based Opponent Shaping
168. NeurIPS 2026Diffusion fine-tuning with Rewarded Moment Matching Distillation
169. NeurIPS 2026Diffusion Subgoal Planning for Long-Horizon Offline Goal-Conditioned Reinforcement Learning
170. NeurIPS 2026Diffusion-State Policy Optimization for Masked Diffusion Language Models
171. NeurIPS 2026DiReCL: Learning Differentiable Reward Code with Inverse Reinforcement Learning
172. NeurIPS 2026DISCOVER: Online Variance-Guided Data Discovery for Budgeted Multimodal GRPO
173. NeurIPS 2026Disentangling the Good From the Bad: Quantization-Induced Flips Are Not Random
174. NeurIPS 2026Distill to Think, Foresee to Act: Cognitive-Physical Reinforcement Learning for Autonomous Driving
175. NeurIPS 2026Distilling LLM Feedback for Lean Theorem Proving
176. NeurIPS 2026Distribution Corrected Decision Transformer for Offline Reinforcement Learning with Imbalanced Datasets
177. NeurIPS 2026Distribution-Adaptive Policy Optimization
178. NeurIPS 2026Distributionally Robust Black Box Optimization-based Bidding Strategy in Auction-based Federated Learning
179. NeurIPS 2026Distributionally Robust Multi-Task Reinforcement Learning via Adaptive Task Sampling
180. NeurIPS 2026Distributionally Robust Token Optimization in RLHF
181. NeurIPS 2026Distributionally-Robust Policy Learning from Observational Data
182. NeurIPS 2026DiversePlace: Diversity-Seeking Curriculum Reinforcement Learning for Macro Placement
183. NeurIPS 2026DocAtlas: Long-Document Understanding as Mutable-State Interaction
184. NeurIPS 2026Dodge-It: Learning Collision-Aware VLA Models for Robotic Manipulation
185. NeurIPS 2026Don’t Let Gains FADE: Breaking Down Policy Gradient Weights in RL
186. NeurIPS 2026DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training
187. NeurIPS 2026Dr. MAS: Stable Reinforcement Learning for Multi-Agent LLM Systems
188. NeurIPS 2026Drift Q-Learning
189. NeurIPS 2026Drifting Field Policy: Wasserstein Gradient Flow on Policy Space for Offline-to-Online RL
190. NeurIPS 2026DRILL: Training World Models to Improve Policies, Not Predict Pixels
191. NeurIPS 2026DRTriton: Large-Scale Synthetic Data Driven Reinforcement Learning for Triton Kernel Generation
192. NeurIPS 2026DualKV: Shared-Prompt Flash Attention for Efficient RL Training with Large Rollouts and Long Contexts
193. NeurIPS 2026DUDS: Dual-stage Data Selection for Efficient Reinforcement Learning with Verifiable Rewards
194. NeurIPS 2026Dyna-Style Safety Augmented Reinforcement Learning: Staying Safe in the Face of Uncertainty
195. NeurIPS 2026DynamAuction: a reinforcement learning environment for repeated auction with dynamic value
196. NeurIPS 2026Dynamic Tokenization via Reinforcement Patching: End-to-end Training and Zero-shot Transfer
197. NeurIPS 2026Dynamic Treatment on Networks
198. NeurIPS 2026Dynamics-Informed Adaptive Offline RL for Real-Time Tokamak Plasma Control
199. NeurIPS 2026Early Prediction of Future Behavioral Strategy from Process Traces
200. NeurIPS 2026ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning

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。