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 1,640 篇,第 29 / 33 页

  1. NeurIPS 2025Reinforcement Learning Meets Masked Generative Models: Mask-GRPO for Text-to-Image Generation
  2. NeurIPS 2025Reinforcement Learning Teachers of Test Time Scaling
  3. NeurIPS 2025Reinforcement Learning with Action Chunking
  4. NeurIPS 2025Reinforcement Learning with Backtracking Feedback
  5. NeurIPS 2025Reinforcement Learning with Imperfect Transition Predictions: A Bellman-Jensen Approach
  6. NeurIPS 2025Reinforcing Spatial Reasoning in Vision-Language Models with Interwoven Thinking and Visual Drawing
  7. NeurIPS 2025Reinforcing the Diffusion Chain of Lateral Thought with Diffusion Language Models
  8. NeurIPS 2025ReMA: Learning to Meta-Think for LLMs with Multi-agent Reinforcement Learning
  9. NeurIPS 2025Rendering-Aware Reinforcement Learning for Vector Graphics Generation
  10. NeurIPS 2025RePIC: Reinforced Post-Training for Personalizing Multi-Modal Language Models
  11. NeurIPS 2025ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning
  12. NeurIPS 2025ResponseRank: Data-Efficient Reward Modeling through Preference Strength Learning
  13. NeurIPS 2025Rethinking Verification for LLM Code Generation: From Generation to Testing
  14. NeurIPS 2025Retro-R1: LLM-based Agentic Retrosynthesis
  15. NeurIPS 2025Retrospective In-Context Learning for Temporal Credit Assignment with Large Language Models
  16. NeurIPS 2025Retrosynthesis Planning via Worst-path Policy Optimisation in Tree-structured MDPs
  17. NeurIPS 2025Retrv-R1: A Reasoning-Driven MLLM Framework for Universal and Efficient Multimodal Retrieval
  18. NeurIPS 2025Reverse Engineering Human Preferences with Reinforcement Learning
  19. NeurIPS 2025Revisiting Multi-Agent World Modeling from a Diffusion-Inspired Perspective
  20. NeurIPS 2025Revisiting Reinforcement Learning for LLM Reasoning from A Cross-Domain Perspective
  21. NeurIPS 2025Reviving DSP for Advanced Theorem Proving in the Era of Reasoning Models
  22. NeurIPS 2025Revolutionizing Training-Free NAS: Towards Efficient Automatic Proxy Discovery via Large Language Models
  23. NeurIPS 2025Reward Reasoning Models
  24. NeurIPS 2025Reward-Aware Proto-Representations in Reinforcement Learning
  25. NeurIPS 2025Reward-Instruct: A Reward-Centric Approach to Fast Photo-Realistic Image Generation
  26. NeurIPS 2025RF-Agent: Automated Reward Function Design via Language Agent Tree Search
  27. NeurIPS 2025Right Question is Already Half the Answer: Fully Unsupervised LLM Reasoning Incentivization
  28. NeurIPS 2025Risk-Averse Constrained Reinforcement Learning with Optimized Certainty Equivalents
  29. NeurIPS 2025Risk-Averse Total-Reward Reinforcement Learning
  30. NeurIPS 2025RL Tango: Reinforcing Generator and Verifier Together for Language Reasoning
  31. NeurIPS 2025RLGF: Reinforcement Learning with Geometric Feedback for Autonomous Driving Video Generation
  32. NeurIPS 2025RLVR-World: Training World Models with Reinforcement Learning
  33. NeurIPS 2025RLZero: Direct Policy Inference from Language Without In-Domain Supervision
  34. NeurIPS 2025RoboRefer: Towards Spatial Referring with Reasoning in Vision-Language Models for Robotics
  35. NeurIPS 2025Robot-R1: Reinforcement Learning for Enhanced Embodied Reasoning in Robotics
  36. NeurIPS 2025RobotSmith: Generative Robotic Tool Design for Acquisition of Complex Manipulation Skills
  37. NeurIPS 2025Robust and Diverse Multi-Agent Learning via Rational Policy Gradient
  38. NeurIPS 2025Robust and Scalable Autonomous Reinforcement Learning in Irreversible Environments
  39. NeurIPS 2025Robust LLM Alignment via Distributionally Robust Direct Preference Optimization
  40. NeurIPS 2025Robust Policy Expansion for Offline-to-Online RL under Diverse Data Corruption
  41. NeurIPS 2025Robust Reinforcement Learning in Finance: Modeling Market Impact with Elliptic Uncertainty Sets
  42. NeurIPS 2025Role-aware Multi-agent Reinforcement Learning for Coordinated Emergency Traffic Control
  43. NeurIPS 2025Router-R1: Teaching LLMs Multi-Round Routing and Aggregation via Reinforcement Learning
  44. NeurIPS 2025RULE: Reinforcement UnLEarning Achieves Forget-retain Pareto Optimality
  45. NeurIPS 2025S-GRPO: Early Exit via Reinforcement Learning in Reasoning Models
  46. NeurIPS 2025Safe and Stable Control via Lyapunov-Guided Diffusion Models
  47. NeurIPS 2025Safe RLHF-V: Safe Reinforcement Learning from Multi-modal Human Feedback
  48. NeurIPS 2025SAFE: Multitask Failure Detection for Vision-Language-Action Models
  49. NeurIPS 2025SafeVLA: Towards Safety Alignment of Vision-Language-Action Model via Constrained Learning
  50. NeurIPS 2025SALMONN-omni: A Standalone Speech LLM without Codec Injection for Full-duplex Conversation

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。