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 1,070 篇，第 20 / 22 页

951. NeurIPS 2023Replicability in Reinforcement Learning
952. NeurIPS 2023Replicable Reinforcement Learning
953. NeurIPS 2023RePo: Resilient Model-Based Reinforcement Learning by Regularizing Posterior Predictability
954. NeurIPS 2023Resetting the Optimizer in Deep RL: An Empirical Study
955. NeurIPS 2023Residual Q-Learning: Offline and Online Policy Customization without Value
956. NeurIPS 2023Revisiting Implicit Differentiation for Learning Problems in Optimal Control
957. NeurIPS 2023Revisiting the Minimalist Approach to Offline Reinforcement Learning
958. NeurIPS 2023Reward Finetuning for Faster and More Accurate Unsupervised Object Discovery
959. NeurIPS 2023Reward Scale Robustness for Proximal Policy Optimization via DreamerV3 Tricks
960. NeurIPS 2023Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning
961. NeurIPS 2023Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement
962. NeurIPS 2023Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards
963. NeurIPS 2023Rewiring Neurons in Non-Stationary Environments
964. NeurIPS 2023Risk-Averse Active Sensing for Timely Outcome Prediction under Cost Pressure
965. NeurIPS 2023Risk-Averse Model Uncertainty for Distributionally Robust Safe Reinforcement Learning
966. NeurIPS 2023RiskQ: Risk-sensitive Multi-Agent Reinforcement Learning Value Factorization
967. NeurIPS 2023RL-based Stateful Neural Adaptive Sampling and Denoising for Real-Time Path Tracing
968. NeurIPS 2023RL-ViGen: A Reinforcement Learning Benchmark for Visual Generalization
969. NeurIPS 2023RoboCLIP: One Demonstration is Enough to Learn Robot Policies
970. NeurIPS 2023RoboHive: A Unified Framework for Robot Learning
971. NeurIPS 2023Robust Knowledge Transfer in Tiered Reinforcement Learning
972. NeurIPS 2023Robust Multi-Agent Reinforcement Learning via Adversarial Regularization: Theoretical Foundation and Stable Algorithms
973. NeurIPS 2023Safe Exploration in Reinforcement Learning: A Generalized Formulation and Algorithms
974. NeurIPS 2023SafeDICE: Offline Safe Imitation Learning with Non-Preferred Demonstrations
975. NeurIPS 2023Safety Gymnasium: A Unified Safe Reinforcement Learning Benchmark
976. NeurIPS 2023Safety Verification of Decision-Tree Policies in Continuous Time
977. NeurIPS 2023Sample Complexity for Quadratic Bandits: Hessian Dependent Bounds and Optimal Algorithms
978. NeurIPS 2023Sample Complexity of Goal-Conditioned Hierarchical Reinforcement Learning
979. NeurIPS 2023Sample Efficient Reinforcement Learning in Mixed Systems through Augmented Samples and Its Applications to Queueing Networks
980. NeurIPS 2023Sample-Efficient and Safe Deep Reinforcement Learning via Reset Deep Ensemble Agents
981. NeurIPS 2023Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
982. NeurIPS 2023SARAMIS: Simulation Assets for Robotic Assisted and Minimally Invasive Surgery
983. NeurIPS 2023Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities
984. NeurIPS 2023Scalarization for Multi-Task and Multi-Domain Learning at Scale
985. NeurIPS 2023ScenarioNet: Open-Source Platform for Large-Scale Traffic Scenario Simulation and Modeling
986. NeurIPS 2023Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation
987. NeurIPS 2023SEENN: Towards Temporal Spiking Early Exit Neural Networks
988. NeurIPS 2023Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning
989. NeurIPS 2023Self-Predictive Universal AI
990. NeurIPS 2023Self-Refine: Iterative Refinement with Self-Feedback
991. NeurIPS 2023Self-Supervised Reinforcement Learning that Transfers using Random Features
992. NeurIPS 2023Semantic HELM: A Human-Readable Memory for Reinforcement Learning
993. NeurIPS 2023Sequential Preference Ranking for Efficient Reinforcement Learning from Human Feedback
994. NeurIPS 2023SHOT: Suppressing the Hessian along the Optimization Trajectory for Gradient-Based Meta-Learning
995. NeurIPS 2023SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning
996. NeurIPS 2023Small batch deep reinforcement learning
997. NeurIPS 2023Social Motion Prediction with Cognitive Hierarchies
998. NeurIPS 2023Solving a Class of Non-Convex Minimax Optimization in Federated Learning
999. NeurIPS 2023Sorting with Predictions
1000. NeurIPS 2023Sparse Graph Learning from Spatiotemporal Time Series

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。