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

每年论文数

各会议论文数

按年份和会议统计

强化学习论文数,按年份和会议
年份NeurIPSICLRICMLCVPR合计
2023484233297561,070
2024466304356641,190
2025784379405721,640
20267609111,0212932,985

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

全部强化学习论文

共 2,079 篇,第 22 / 42 页

  1. ICML 2025AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N
  2. ICML 2025Aligned Multi Objective Optimization
  3. ICML 2025AlphaDPO: Adaptive Reward Margin for Direct Preference Optimization
  4. ICML 2025AlphaPO: Reward Shape Matters for LLM Alignment
  5. ICML 2025AlphaQCM: Alpha Discovery in Finance with Distributional Reinforcement Learning
  6. ICML 2025An Online Learning Approach to Prompt-based Selection of Generative Models and LLMs
  7. ICML 2025An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints
  8. ICML 2025Analytical Lyapunov Function Discovery: An RL-based Generative Approach
  9. ICML 2025ARS: Adaptive Reward Scaling for Multi-Task Reinforcement Learning
  10. ICML 2025AssistanceZero: Scalably Solving Assistance Games
  11. ICML 2025AUTOCIRCUIT-RL: Reinforcement Learning-Driven LLM for Automated Circuit Topology Generation
  12. ICML 2025Automatic Reward Shaping from Confounded Offline Data
  13. ICML 2025Behavior-agnostic Task Inference for Robust Offline In-context Reinforcement Learning
  14. ICML 2025Behavior-Regularized Diffusion Policy Optimization for Offline Reinforcement Learning
  15. ICML 2025Behavioral Exploration: Learning to Explore via In-Context Adaptation
  16. ICML 2025Bellman Unbiasedness: Toward Provably Efficient Distributional Reinforcement Learning with General Value Function Approximation
  17. ICML 2025Benchmarking Quantum Reinforcement Learning
  18. ICML 2025Beyond Bradley-Terry Models: A General Preference Model for Language Model Alignment
  19. ICML 2025Beyond CVaR: Leveraging Static Spectral Risk Measures for Enhanced Decision-Making in Distributional Reinforcement Learning
  20. ICML 2025Beyond The Rainbow: High Performance Deep Reinforcement Learning on a Desktop PC
  21. ICML 2025Branches: Efficiently Seeking Optimal Sparse Decision Trees via AO*
  22. ICML 2025Breaking the Curse of Multiagency in Robust Multi-Agent Reinforcement Learning
  23. ICML 2025BRiTE: Bootstrapping Reinforced Thinking Process to Enhance Language Model Reasoning
  24. ICML 2025C-3PO: Compact Plug-and-Play Proxy Optimization to Achieve Human-like Retrieval-Augmented Generation
  25. ICML 2025C2IQL: Constraint-Conditioned Implicit Q-learning for Safe Offline Reinforcement Learning
  26. ICML 2025Calibrated Value-Aware Model Learning with Probabilistic Environment Models
  27. ICML 2025Can RLHF be More Efficient with Imperfect Reward Models? A Policy Coverage Perspective
  28. ICML 2025Catching Two Birds with One Stone: Reward Shaping with Dual Random Networks for Balancing Exploration and Exploitation
  29. ICML 2025Categorical Distributional Reinforcement Learning with Kullback-Leibler Divergence: Convergence and Asymptotics
  30. ICML 2025Catoni Contextual Bandits are Robust to Heavy-tailed Rewards
  31. ICML 2025CERTAIN: Context Uncertainty-aware One-Shot Adaptation for Context-based Offline Meta Reinforcement Learning
  32. ICML 2025Chip Placement with Diffusion Models
  33. ICML 2025CLARIFY: Contrastive Preference Reinforcement Learning for Untangling Ambiguous Queries
  34. ICML 2025Clone-Robust AI Alignment
  35. ICML 2025Closed-form Solutions: A New Perspective on Solving Differential Equations
  36. ICML 2025CogReact: A Reinforced Framework to Model Human Cognitive Reaction Modulated by Dynamic Intervention
  37. ICML 2025CollabLLM: From Passive Responders to Active Collaborators
  38. ICML 2025Combinatorial Reinforcement Learning with Preference Feedback
  39. ICML 2025Comparing Comparisons: Informative and Easy Human Feedback with Distinguishability Queries
  40. ICML 2025Comparing Few to Rank Many: Active Human Preference Learning Using Randomized Frank-Wolfe Method
  41. ICML 2025Concurrent Reinforcement Learning with Aggregated States via Randomized Least Squares Value Iteration
  42. ICML 2025Consensus Based Stochastic Optimal Control
  43. ICML 2025Conservative Offline Goal-Conditioned Implicit V-Learning
  44. ICML 2025Constrain Alignment with Sparse Autoencoders
  45. ICML 2025Constrained Exploitability Descent: An Offline Reinforcement Learning Method for Finding Mixed-Strategy Nash Equilibrium
  46. ICML 2025Contextual Optimization Under Model Misspecification: A Tractable and Generalizable Approach
  47. ICML 2025Continual Reinforcement Learning by Planning with Online World Models
  48. ICML 2025Controlling Large Language Model with Latent Action
  49. ICML 2025Controlling Underestimation Bias in Constrained Reinforcement Learning for Safe Exploration
  50. ICML 2025Convergence Analysis of Policy Gradient Methods with Dynamic Stochasticity

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"}

两分钟接入 Paperlist MCP(Claude Code、Cursor、Codex、ChatGPT)

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。