通用机器学习 · reinforcement learning论文趋势 2023–2026

通用机器学习：reinforcement_learning

统计范围：NeurIPS、ICLR、ICML、CVPR 四个会议，2023–2026 年，共 1,676 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

通用机器学习 · reinforcement learning论文数，按年份和会议
年份NeurIPSICLRICMLCVPR合计
202314666723287
2024101216824403
2025108230712411
202611333511314575

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

全部通用机器学习 · reinforcement learning论文

年份全部2023202420252026
会议全部NeurIPSICLRICMLCVPR

共 1,676 篇，第 19 / 34 页

  1. NeurIPS 2025Computational Hardness of Reinforcement Learning with Partial $q^{\pi}$-Realizability
  2. NeurIPS 2025DAIL: Beyond Task Ambiguity for Language-Conditioned Reinforcement Learning
  3. NeurIPS 2025DAPO: An Open-Source LLM Reinforcement Learning System at Scale
  4. NeurIPS 2025DISCOVER: Automated Curricula for Sparse-Reward Reinforcement Learning
  5. NeurIPS 2025Doubly Robust Alignment for Large Language Models
  6. NeurIPS 2025Dynamic Configuration for Cutting Plane Separators via Reinforcement Learning on Incremental Graph
  7. NeurIPS 2025Efficient Preference-Based Reinforcement Learning: Randomized Exploration meets Experimental Design
  8. NeurIPS 2025Eluder dimension: localise it!
  9. NeurIPS 2025Enhancing Interpretability in Deep Reinforcement Learning through Semantic Clustering
  10. NeurIPS 2025Explainably Safe Reinforcement Learning
  11. NeurIPS 2025FANS: A Flatness-Aware Network Structure for Generalization in Offline Reinforcement Learning
  12. NeurIPS 2025Finite Sample Analysis of Linear Temporal Difference Learning with Arbitrary Features
  13. NeurIPS 2025From Experts to a Generalist: Toward General Whole-Body Control for Humanoid Robots
  14. NeurIPS 2025Fuz-RL: A Fuzzy-Guided Robust Framework for Safe Reinforcement Learning under Uncertainty
  15. NeurIPS 2025General-Reasoner: Advancing LLM Reasoning Across All Domains
  16. NeurIPS 2025Global Convergence for Average Reward Constrained MDPs with Primal-Dual Actor Critic Algorithm
  17. NeurIPS 2025Greedy Sampling Is Provably Efficient For RLHF
  18. NeurIPS 2025Gymnasium: A Standard Interface for Reinforcement Learning Environments
  19. NeurIPS 2025Horizon Reduction Makes RL Scalable
  20. NeurIPS 2025How Ensembles of Distilled Policies Improve Generalisation in Reinforcement Learning
  21. NeurIPS 2025Imagined Autocurricula
  22. NeurIPS 2025Imitation Beyond Expectation Using Pluralistic Stochastic Dominance
  23. NeurIPS 2025Imitation Learning with Temporal Logic Constraints
  24. NeurIPS 2025Improving Model-Based Reinforcement Learning by Converging to Flatter Minima
  25. NeurIPS 2025Improving Reward Models with Proximal Policy Exploration for Preference-Based Reinforcement Learning
  26. NeurIPS 2025Intrinsic Benefits of Categorical Distributional Loss: Uncertainty-aware Regularized Exploration in Reinforcement Learning
  27. NeurIPS 2025Learning Parameterized Skills from Demonstrations
  28. NeurIPS 2025Learning Personalized Ad Impact via Contextual Reinforcement Learning under Delayed Rewards
  29. NeurIPS 2025Learning to Clean: Reinforcement Learning for Noisy Label Correction
  30. NeurIPS 2025Measure gradients, not activations! Enhancing neuronal activity in deep reinforcement learning
  31. NeurIPS 2025Meta-learning how to Share Credit among Macro-Actions
  32. NeurIPS 2025Mind the GAP! The Challenges of Scale in Pixel-based Deep Reinforcement Learning
  33. NeurIPS 2025Mixture-of-Experts Meets In-Context Reinforcement Learning
  34. NeurIPS 2025Novel Exploration via Orthogonality
  35. NeurIPS 2025Offline Actor-Critic for Average Reward MDPs
  36. NeurIPS 2025Offline Goal-conditioned Reinforcement Learning with Quasimetric Representations
  37. NeurIPS 2025Offline imitation learning in $Q^\pi$-realizable MDPs without expert realizability
  38. NeurIPS 2025Omni-R1: Reinforcement Learning for Omnimodal Reasoning via Two-System Collaboration
  39. NeurIPS 2025On the Global Optimality of Policy Gradient Methods in General Utility Reinforcement Learning
  40. NeurIPS 2025On the Sample Complexity Bounds of Bilevel Reinforcement Learning
  41. NeurIPS 2025Optimal Dynamic Regret by Transformers for Non-Stationary Reinforcement Learning
  42. NeurIPS 2025Optimal Regret Bounds via Low-Rank Structured Variation in Non-Stationary Reinforcement Learning
  43. NeurIPS 2025Option-aware Temporally Abstracted Value for Offline Goal-Conditioned Reinforcement Learning
  44. NeurIPS 2025Perception-R1: Pioneering Perception Policy with Reinforcement Learning
  45. NeurIPS 2025Preference Distillation via Value based Reinforcement Learning
  46. NeurIPS 2025Preference-based Reinforcement Learning beyond Pairwise Comparisons: Benefits of Multiple Options
  47. NeurIPS 2025Pretraining a Shared Q-Network for Data-Efficient Offline Reinforcement Learning
  48. NeurIPS 2025Progress Reward Model for Reinforcement Learning via Large Language Models
  49. NeurIPS 2025Quantization-Free Autoregressive Action Transformer
  50. NeurIPS 2025Real-World Reinforcement Learning of Active Perception Behaviors

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 查通用机器学习 · reinforcement learning在 ICLR 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": "gml-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通用机器学习 · reinforcement learning论文？
共 1,676 篇，按年份分别是 2023 年 287 篇、2024 年 403 篇、2025 年 411 篇、2026 年 575 篇。
NeurIPS 2026 有多少篇通用机器学习 · reinforcement learning论文？
NeurIPS 2026 有 113 篇通用机器学习 · reinforcement learning论文，上一届是 108 篇。
ICLR 2026 有多少篇通用机器学习 · reinforcement learning论文？
ICLR 2026 有 335 篇通用机器学习 · reinforcement learning论文，上一届是 230 篇。
ICML 2026 有多少篇通用机器学习 · reinforcement learning论文？
ICML 2026 有 113 篇通用机器学习 · reinforcement learning论文，上一届是 71 篇。
CVPR 2026 有多少篇通用机器学习 · reinforcement learning论文？
CVPR 2026 有 14 篇通用机器学习 · reinforcement learning论文，上一届是 2 篇。
哪个会议的通用机器学习 · reinforcement learning论文最多？
ICLR，2023–2026 年共 847 篇。

「通用机器学习」下的其他主题