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 2,079 篇，第 36 / 42 页
- ICML 2024Switching the Loss Reduces the Cost in Batch Reinforcement Learning
- ICML 2024Symmetric Replay Training: Enhancing Sample Efficiency in Deep Reinforcement Learning for Combinatorial Optimization
- ICML 2024Tackling Non-Stationarity in Reinforcement Learning via Causal-Origin Representation
- ICML 2024Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function Approximation
- ICML 2024Temporal Logic Specification-Conditioned Decision Transformer for Offline Safe Reinforcement Learning
- ICML 2024Test-Time Regret Minimization in Meta Reinforcement Learning
- ICML 2024The Max-Min Formulation of Multi-Objective Reinforcement Learning: From Theory to a Model-Free Algorithm
- ICML 2024The Non-linear $F$-Design and Applications to Interactive Learning
- ICML 2024To the Max: Reinventing Reward in Reinforcement Learning
- ICML 2024Towards Efficient Exact Optimization of Language Model Alignment
- ICML 2024Towards General Algorithm Discovery for Combinatorial Optimization: Learning Symbolic Branching Policy from Bipartite Graph
- ICML 2024Towards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oracles
- ICML 2024Towards Optimal Adversarial Robust Q-learning with Bellman Infinity-error
- ICML 2024Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption
- ICML 2024Training Greedy Policy for Proposal Batch Selection in Expensive Multi-Objective Combinatorial Optimization
- ICML 2024Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning
- ICML 2024Truly No-Regret Learning in Constrained MDPs
- ICML 2024Trust the Model Where It Trusts Itself - Model-Based Actor-Critic with Uncertainty-Aware Rollout Adaption
- ICML 2024Trustworthy Alignment of Retrieval-Augmented Large Language Models via Reinforcement Learning
- ICML 2024ULTRAFEEDBACK: Boosting Language Models with Scaled AI Feedback
- ICML 2024Uncertainty-Aware Reward-Free Exploration with General Function Approximation
- ICML 2024Understanding and Diagnosing Deep Reinforcement Learning
- ICML 2024Unlock the Cognitive Generalization of Deep Reinforcement Learning via Granular Ball Representation
- ICML 2024Unsupervised Zero-Shot Reinforcement Learning via Functional Reward Encodings
- ICML 2024Value-Evolutionary-Based Reinforcement Learning
- ICML 2024WARM: On the Benefits of Weight Averaged Reward Models
- ICML 2024Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision
- ICML 2024When Do Skills Help Reinforcement Learning? A Theoretical Analysis of Temporal Abstractions
- ICML 2024When is Transfer Learning Possible?
- ICML 2024Why Do Animals Need Shaping? A Theory of Task Composition and Curriculum Learning
- ICML 2024Zero-Shot Reinforcement Learning via Function Encoders
- ICML 2024Zero-Sum Positional Differential Games as a Framework for Robust Reinforcement Learning: Deep Q-Learning Approach
- ICML 2023A Connection between One-Step RL and Critic Regularization in Reinforcement Learning
- ICML 2023A Coupled Flow Approach to Imitation Learning
- ICML 2023A Game-Theoretic Framework for Managing Risk in Multi-Agent Systems
- ICML 2023A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints
- ICML 2023A Reinforcement Learning Framework for Dynamic Mediation Analysis
- ICML 2023A Robust Test for the Stationarity Assumption in Sequential Decision Making
- ICML 2023A Universal Unbiased Method for Classification from Aggregate Observations
- ICML 2023Abstracting Imperfect Information Away from Two-Player Zero-Sum Games
- ICML 2023Action Matching: Learning Stochastic Dynamics from Samples
- ICML 2023Active Policy Improvement from Multiple Black-box Oracles
- ICML 2023Actor-Critic Alignment for Offline-to-Online Reinforcement Learning
- ICML 2023AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners
- ICML 2023Adaptive Barrier Smoothing for First-Order Policy Gradient with Contact Dynamics
- ICML 2023Adversarial Cheap Talk
- ICML 2023Adversarial Learning of Distributional Reinforcement Learning
- ICML 2023Aligning Language Models with Preferences through $f$-divergence Minimization
- ICML 2023An Adaptive Entropy-Regularization Framework for Multi-Agent Reinforcement Learning
- ICML 2023An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning
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。