强化学习论文趋势 2023–2026
策略优化、离线 RL 与基于环境的决策学习
统计范围：NeurIPS、ICLR、ICML、CVPR 四个会议，2023–2026 年，共 6,885 篇被归入该主题的论文。
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。
各会议论文数
按年份和会议统计
| 年份 | NeurIPS | ICLR | ICML | CVPR | 合计 |
|---|---|---|---|---|---|
| 2023 | 484 | 233 | 297 | 56 | 1,070 |
| 2024 | 466 | 304 | 356 | 64 | 1,190 |
| 2025 | 784 | 379 | 405 | 72 | 1,640 |
| 2026 | 760 | 911 | 1,021 | 293 | 2,985 |
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 1,640 篇，第 25 / 33 页
- NeurIPS 2025Learning Preferences without Interaction for Cooperative AI: A Hybrid Offline-Online Approach
- NeurIPS 2025Learning to Better Search with Language Models via Guided Reinforced Self-Training
- NeurIPS 2025Learning to Clean: Reinforcement Learning for Noisy Label Correction
- NeurIPS 2025Learning to Control Free-Form Soft Swimmers
- NeurIPS 2025Learning to Plan Like the Human Brain via Visuospatial Perception and Semantic-Episodic Synergistic Decision-Making
- NeurIPS 2025Learning to Reason under Off-Policy Guidance
- NeurIPS 2025Learning to Think: Information-Theoretic Reinforcement Fine-Tuning for LLMs
- NeurIPS 2025Learning When to Think: Shaping Adaptive Reasoning in R1-Style Models via Multi-Stage RL
- NeurIPS 2025Less is More: an Attention-free Sequence Prediction Modeling for Offline Embodied Learning
- NeurIPS 2025Leveraging Conditional Dependence for Efficient World Model Denoising
- NeurIPS 2025LILO: Learning to Reason at the Frontier of Learnability
- NeurIPS 2025Linear Mixture Distributionally Robust Markov Decision Processes
- NeurIPS 2025LLM-Explorer: A Plug-in Reinforcement Learning Policy Exploration Enhancement Driven by Large Language Models
- NeurIPS 2025Logic.py: Bridging the Gap between LLMs and Constraint Solvers
- NeurIPS 2025Look Before You Leap: A GUI-Critic-R1 Model for Pre-Operative Error Diagnosis in GUI Automation
- NeurIPS 2025LOPT: Learning Optimal Pigovian Tax in Sequential Social Dilemmas
- NeurIPS 2025MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures
- NeurIPS 2025MALinZero: Efficient Low-Dimensional Search for Mastering Complex Multi-Agent Planning
- NeurIPS 2025Markov Persuasion Processes: Learning to Persuade From Scratch
- NeurIPS 2025Mean-Field Sampling for Cooperative Multi-Agent Reinforcement Learning
- NeurIPS 2025Measure gradients, not activations! Enhancing neuronal activity in deep reinforcement learning
- NeurIPS 2025Memo: Training Memory-Efficient Embodied Agents with Reinforcement Learning
- NeurIPS 2025Memory-Enhanced Neural Solvers for Routing Problems
- NeurIPS 2025Mesh-RFT: Enhancing Mesh Generation via Fine-grained Reinforcement Fine-Tuning
- NeurIPS 2025Meta-learning how to Share Credit among Macro-Actions
- NeurIPS 2025Meta-Learning Objectives for Preference Optimization
- NeurIPS 2025Meta-World+: An Improved, Standardized, RL Benchmark
- NeurIPS 2025MetaBox-v2: A Unified Benchmark Platform for Meta-Black-Box Optimization
- NeurIPS 2025MetaKoopman: Bayesian Meta-Learning of Koopman Operators for Modeling Structured Dynamics under Distribution Shifts
- NeurIPS 2025MiCo: Multi-image Contrast for Reinforcement Visual Reasoning
- NeurIPS 2025Mind the Gap: Bridging Thought Leap for Improved Chain-of-Thought Tuning
- NeurIPS 2025Mind the GAP! The Challenges of Scale in Pixel-based Deep Reinforcement Learning
- NeurIPS 2025MindJourney: Test-Time Scaling with World Models for Spatial Reasoning
- NeurIPS 2025MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO
- NeurIPS 2025MisoDICE: Multi-Agent Imitation from Mixed-Quality Demonstrations
- NeurIPS 2025Mitigating Reward Over-optimization in Direct Alignment Algorithms with Importance Sampling
- NeurIPS 2025Mixing Expert Knowledge: Bring Human Thoughts Back To the Game of Go
- NeurIPS 2025Mixture-of-Experts Meets In-Context Reinforcement Learning
- NeurIPS 2025MJ-Bench: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?
- NeurIPS 2025MLE-Dojo: Interactive Environments for Empowering LLM Agents in Machine Learning Engineering
- NeurIPS 2025MMaDA: Multimodal Large Diffusion Language Models
- NeurIPS 2025Model Selection for Off-policy Evaluation: New Algorithms and Experimental Protocol
- NeurIPS 2025Model-Based Policy Adaptation for Closed-Loop End-to-end Autonomous Driving
- NeurIPS 2025Modelling the control of offline processing with reinforcement learning
- NeurIPS 2025MonoLift: Learning 3D Manipulation Policies from Monocular RGB via Distillation
- NeurIPS 2025MOSDT: Self-Distillation-Based Decision Transformer for Multi-Agent Offline Safe Reinforcement Learning
- NeurIPS 2025MRO: Enhancing Reasoning in Diffusion Language Models via Multi-Reward Optimization
- NeurIPS 2025MTL-KD: Multi-Task Learning Via Knowledge Distillation for Generalizable Neural Vehicle Routing Solver
- NeurIPS 2025MTRec: Learning to Align with User Preferences via Mental Reward Models
- NeurIPS 2025Multi-Agent Collaboration via Evolving Orchestration
在 Claude、Cursor 里直接问这个趋势
接上 Paperlist MCP 后，写综述、挑方向或准备投稿时，不用离开对话框就能拿到这页的数字：每年各会议的论文数、增长倍数、涨得最快的子方向和代表论文，AI 会按你的问题整理。
可以直接复制这句话问 AI
用 paperlist 查强化学习在 NeurIPS 2023–2026 的论文趋势，哪一年涨得最快？
AI 会调用的工具：get_topic_stats {"topic": "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。