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

主要结论

• 通用机器学习 · reinforcement learning论文从 2023 年的 287 篇增长到 2026 年的 575 篇，三年间扩大到约 2.0 倍。
• ICLR 是发通用机器学习 · reinforcement learning论文最多的会议，2023–2026 年共 847 篇，占四个会议总量的 51%。
• 交叉方向里涨得最快的是扩散模型：同时属于两个主题的论文从 2023 年的 2 篇增至 2026 年的 34 篇。
• 在「通用机器学习」的 6 个主题里，它的 2026 年论文数排第 2；同组增长最快的通用机器学习 · evaluation是 4.3 倍，通用机器学习 · reinforcement learning是 2.0 倍。

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 2026The Sample Complexity of Multiple Change Point Identification under Bandit Feedback
• ICML 2026De-attribute to Forget for LLM Unlearning
• ICLR 2026AbstRaL: Augmenting LLMs' Reasoning by Reinforcing Abstract Thinking
• CVPR 2026Dynamic Important Example Mining for Reinforcement Finetuning
• NeurIPS 2025RULE: Reinforcement UnLEarning Achieves Forget-retain Pareto Optimality
• ICML 2025The Number of Trials Matters in Infinite-Horizon General-Utility Markov Decision Processes
• ICLR 2025A General Framework for Off-Policy Learning with Partially-Observed Reward
• CVPR 2025Automated Proof of Polynomial Inequalities via Reinforcement Learning
• NeurIPS 2024Learning General Parameterized Policies for Infinite Horizon Average Reward Constrained MDPs via Primal-Dual Policy Gradient Algorithm
• ICML 2024Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation
• ICLR 2024Bandits with Replenishable Knapsacks: the Best of both Worlds
• CVPR 2024Improving Unsupervised Hierarchical Representation with Reinforcement Learning
• NeurIPS 2023General Munchausen Reinforcement Learning with Tsallis Kullback-Leibler Divergence
• ICML 2023Non-stationary Reinforcement Learning under General Function Approximation
• ICLR 2023A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning
• CVPR 2023Frustratingly Easy Regularization on Representation Can Boost Deep Reinforcement Learning
• NeurIPS 2026Learning When to Stop: Selective Imitation Learning Under Arbitrary Dynamics Shift
• ICML 2026Fox in the Henhouse: Supply-Chain Backdoor Attacks Against Reinforcement Learning
• ICLR 2026APPLE: Toward General Active Perception via Reinforcement Learning
• CVPR 2026Reinforcement-Guided Synthetic Data Generation for Privacy-Sensitive Identity Recognition

全部通用机器学习 · reinforcement learning论文

共 575 篇，第 3 / 12 页

101. ICLR 2026Efficient Morphology–Control Co-Design via Stackelberg PPO under Non-Differentiable Leader–Follower Interfaces
102. ICLR 2026Efficient Offline Reinforcement Learning via Peer-Influenced Constraint
103. ICLR 2026Efficient Reinforcement Learning by Guiding World Models with Non-Curated Data
104. ICLR 2026Ego-Foresight: Self-supervised Learning of Agent-Aware Representations for Improved RL
105. ICLR 2026ELMUR: External Layer Memory with Update/Rewrite for Long-Horizon RL
106. ICLR 2026EMFuse: Energy-based Model Fusion for Decision Making
107. ICLR 2026Enhancing Geometric Perception in VLMs via Translator-Guided Reinforcement Learning
108. ICLR 2026Enough is as good as a feast: A Comprehensive Analysis of How Reinforcement Learning Mitigates Task Conflicts in LLMs
109. ICLR 2026Entropy Regularizing Activation: Boosting Continuous Control, Large Language Models, and Image Classification with Activation as Entropy Constraints
110. ICLR 2026Entropy-preserving reinforcement learning
111. ICLR 2026EUBRL: Epistemic Uncertainty Directed Bayesian Reinforcement Learning
112. ICLR 2026Exploratory Diffusion Model for Unsupervised Reinforcement Learning
113. ICLR 2026Exploratory Memory-Augmented LLM Agent via Hybrid On- and Off-Policy Optimization
114. ICLR 2026EXPO: Stable Reinforcement Learning with Expressive Policies
115. ICLR 2026Fathom-DeepResearch: Unlocking Long Horizon Information Retrieval and Synthesis for SLMs
116. ICLR 2026Fine-tuning Behavioral Cloning Policies with Preference‑Based Reinforcement Learning
117. ICLR 2026floq: Training Critics via Flow-Matching for Scaling Compute in Value-Based RL
118. ICLR 2026Flow Actor-Critic for Offline Reinforcement Learning
119. ICLR 2026Flow Matching Policy Gradients
120. ICLR 2026Flow Matching with Injected Noise for Offline-to-Online Reinforcement Learning
121. ICLR 2026Flow-Based Single-Step Completion for Efficient and Expressive Policy Learning
122. ICLR 2026Flowing Through States: Neural ODE Regularization for Reinforcement Learning
123. ICLR 2026FlowRL: Matching Reward Distributions for LLM Reasoning
124. ICLR 2026From Observations to Events: Event-Aware World Models for Reinforcement Learning
125. ICLR 2026From Parameters to Behaviors: Unsupervised Compression of the Policy Space
126. ICLR 2026From Ticks to Flows: Dynamics of Neural Reinforcement Learning in Continuous Environments
127. ICLR 2026GAS: Enhancing Reward-Cost Balance of Generative Model-assisted Offline Safe RL
128. ICLR 2026General search techniques without common knowledge for imperfect-information games, and application to superhuman Fog of War chess
129. ICLR 2026Geometric-Mean Policy Optimization
130. ICLR 2026Geometry of Uncertainty: Learning Metric Spaces for Multimodal State Estimation in RL
131. ICLR 2026GEPO: Group Expectation Policy Optimization for Stable Heterogeneous Reinforcement Learning
132. ICLR 2026Getting Your LLMs Ready for Reinforcement Learning with Lightweight SFT
133. ICLR 2026GlobeDiff: State Diffusion Process for Partial Observability in Multi-Agent System
134. ICLR 2026Goal Reaching with Eikonal-Constrained Hierarchical Quasimetric Reinforcement Learning
135. ICLR 2026GoldenStart: Q-Guided Priors and Entropy Control for Distilling Flow Policies
136. ICLR 2026GRACE: A Language Model Framework for Explainable Inverse Reinforcement Learning
137. ICLR 2026Graph-Theoretic Intrinsic Reward: Guiding RL with Effective Resistance
138. ICLR 2026GRL-SNAM: Geometric Reinforcement Learning with Differential Hamiltonians for Navigation and Mapping in Unknown Environments
139. ICLR 2026Guided Flow Policy: Learning from High-Value Actions in Offline Reinforcement Learning
140. ICLR 2026Guided Policy Optimization under Partial Observability
141. ICLR 2026Heterogeneous Agent Q-weighted Policy Optimization
142. ICLR 2026Hierarchical Entity-centric Reinforcement Learning with Factored Subgoal Diffusion
143. ICLR 2026Hierarchy-of-Groups Policy Optimization for Long-Horizon Agentic Tasks
144. ICLR 2026How to Lose Inherent Counterfactuality in Reinforcement Learning
145. ICLR 2026Hybrid Reinforcement: when reward is sparse, better to be dense
146. ICLR 2026Imitation Learning as Return Distribution Matching
147. ICLR 2026Improving and Accelerating Offline RL in Large Discrete Action Spaces with Structured Policy Initialization
148. ICLR 2026Improving Human-AI Coordination through Online Adversarial Training and Generative Models
149. ICLR 2026In-Context Compositional Q-Learning for Offline Reinforcement Learning
150. ICLR 2026In-Context Learning for Pure Exploration

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。