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 2,985 篇，第 52 / 60 页
- NeurIPS 2026LADDERS: Length-Aware Data Distribution and Existing-Response Speculation for Fast RL Rollout Generation
- NeurIPS 2026LaST-VLA: Thinking in Latent Spatio-Temporal Space for Vision-Language-Action in Autonomous Driving
- NeurIPS 2026Latency-Conditioned Selection Bias in RLHF
- NeurIPS 2026Latent Reasoning in Continuous Space for Unified Multimodal Models
- NeurIPS 2026LEAD: Length-Efficient Adaptive and Dynamic Reasoning for Large Language Models
- NeurIPS 2026Learning Better Certified Models from Empirically-Robust Teachers
- NeurIPS 2026Learning Chance-Constrained MDPs with Bellman Distributional Certificates
- NeurIPS 2026Learning CLI Agents with Structured Action Credit under Selective Observation
- NeurIPS 2026Learning Collision‑Free Dispatch Policies for Route‑Wise Decision‑Dependent Anomaly Detection
- NeurIPS 2026Learning Contextual Causal Dynamics for Robust Exploration in Reinforcement Learning
- NeurIPS 2026Learning Cost-Efficient Autoscaling for Latency-Constrained Disaggregated LLM Serving
- NeurIPS 2026Learning Distributions from Multiple Data Providers
- NeurIPS 2026Learning From Failures: Efficient Reinforcement Learning Control with Episodic Memory
- NeurIPS 2026Learning from Language Feedback via Variational Policy Distillation
- NeurIPS 2026Learning Generalizable Hand-Object Tracking Control without Human Demonstrations
- NeurIPS 2026Learning in Causal Markov Games
- NeurIPS 2026Learning in Context, Guided by Choice: A Reward-Free Paradigm for Reinforcement Learning with Transformers
- NeurIPS 2026Learning Large-Scale Competitive Team Behaviors with Mean-Field Interactions
- NeurIPS 2026Learning Multimodal One-step Flow Policy via Value-weighted Optimal Transport
- NeurIPS 2026Learning Neuronal Wiring Rules from Morphological Token Sequences
- NeurIPS 2026Learning Planning Budgets in Real-Time RL
- NeurIPS 2026Learning Process Rewards via Visitation Matching for Efficient RL
- NeurIPS 2026Learning Reusable Motor Motifs for Continuous Animal Behavior Modeling
- NeurIPS 2026Learning Robust Reasoning through Guided Adversarial Self-Play
- NeurIPS 2026Learning to Audit ML Models with Theory of Mind
- NeurIPS 2026Learning to Bid in Repeated Second-Price Auctions with Dynamic Values and Aggregated Feedback
- NeurIPS 2026Learning to Cut: Reinforcement Learning for Benders Decomposition
- NeurIPS 2026Learning to Explore with Parameter-Space Noise: A Deep Dive into Parameter-Space Noise for Reinforcement Learning with Verifiable Rewards
- NeurIPS 2026Learning to Follow In-Context Watermark Instructions via Self-Distillation
- NeurIPS 2026Learning to Group and Order: Cross-Instance Self-Supervised RL for Vision-Centric MLLMs
- NeurIPS 2026Learning to Learn from Multimodal Experience
- NeurIPS 2026Learning to Persuade Exposes How Easily LLMs Abandon Correct Beliefs
- NeurIPS 2026Learning to Surpass: Training Tool-Using Agents with Anchored Feedback
- NeurIPS 2026Learning to Trigger: Reinforcement Learning at the Large Hadron Collider
- NeurIPS 2026Learning to Undo: Transfer Reinforcement Learning under State Space Transformations
- NeurIPS 2026Learning When to Stop: Selective Imitation Learning Under Arbitrary Dynamics Shift
- NeurIPS 2026Learning When to Think: Adaptive Internal Computation for Reinforcement Learning
- NeurIPS 2026Leveraging Dale’s Principle as an Inductive Bias in Recurrent Neural Networks
- NeurIPS 2026Leveraging Latent Visual Reasoning in Silence
- NeurIPS 2026Leveraging Psychophysical Attentional Distribution for Gaze-Augmented Reward Modeling
- NeurIPS 2026LiFT: Likelihood-Free Tree-Structured Policy Optimization for Flow-Based VLAs
- NeurIPS 2026Likelihood-Free Generative Policy Optimization
- NeurIPS 2026LiveOption: Evaluating LLM Agents in Structured Option Trading with Nonlinear Payoffs
- NeurIPS 2026LLMs as MDP Designers: A Dependency-Aware Agentic Framework for Automated Robot Policy Generation
- NeurIPS 2026Local Policy Manifolds for Efficient Multi-Objective Reinforcement Learning
- NeurIPS 2026Localized Dynamics-Aware Domain Adaption for Off-Dynamics Offline Reinforcement Learning
- NeurIPS 2026LOCU: Löwdin-Orthogonalized Constraint Updates for Multi-Constraint Policy Optimization
- NeurIPS 2026LogT: Logically Think with Images for Visual Search
- NeurIPS 2026Long-Horizon Q-Learning: Accurate Value Learning via n-Step Inequalities
- NeurIPS 2026Look Before You Reason: Implicit Visual Thinking for Efficient Multimodal Reasoning
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。