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 468 篇，第 3 / 10 页

101. NeurIPS 2026Towards Optimal Pre-training Data Mixtures for Empowering Reinforcement Learning in LLMs
102. NeurIPS 2026Trajectory-Consistent Diffusion Policies for Offline Reinforcement Learning
103. NeurIPS 2026Trust Region Q Adjoint Matching
104. NeurIPS 2026TrustFlow: Adaptive Trust Calibration for Language Model Guided Reinforcement Learning
105. NeurIPS 2026Uncertainty-Guided Reward Labeling for Reinforcement Learning under Limited Feedback
106. NeurIPS 2026Underlying Functional Structure of Reinforcement Learning
107. NeurIPS 2026Value-Rectified Distillation for Flow-based Offline Reinforcement Learning
108. NeurIPS 2026Variance-Averse n-Step Offline Reinforcement Learning for Sparse Long-Horizon Environments
109. NeurIPS 2026Variational Consequence-Driven Offline Reinforcement Learning
110. NeurIPS 2026Visual Reinforcement Fine-Tuning via Bootstrapped Medical Reasoning
111. NeurIPS 2026When Does Non-Uniform Replay Matter in Reinforcement Learning?
112. NeurIPS 2026When Policies Cannot Be Retrained: A Unified Closed-Form View of Post-Training Steering in Offline Reinforcement Learning
113. NeurIPS 2026Your Language Model is Its Own Critic: Reinforcement Learning with Value Estimation from Actor’s Internal States
114. NeurIPS 2025A Bayesian Fast-Slow Framework to Mitigate Interference in Non-Stationary Reinforcement Learning
115. NeurIPS 2025A Clean Slate for Offline Reinforcement Learning
116. NeurIPS 2025A Novel General Framework for Sharp Lower Bounds in Succinct Stochastic Bandits
117. NeurIPS 2025A Provable Approach for End-to-End Safe Reinforcement Learning
118. NeurIPS 2025A Reinforcement Learning-based Bidding Strategy for Data Consumers in Auction-based Federated Learning
119. NeurIPS 2025Actor-Free Continuous Control via Structurally Maximizable Q-Functions
120. NeurIPS 2025Adaptive Batch-Wise Sample Scheduling for Direct Preference Optimization
121. NeurIPS 2025Adaptive Neighborhood-Constrained Q Learning for Offline Reinforcement Learning
122. NeurIPS 2025Adaptive Surrogate Gradients for Sequential Reinforcement Learning in Spiking Neural Networks
123. NeurIPS 2025Approximating Shapley Explanations in Reinforcement Learning
124. NeurIPS 2025Automaton Constrained Q-Learning
125. NeurIPS 2025Behavior Injection: Preparing Language Models for Reinforcement Learning
126. NeurIPS 2025Beyond Average Value Function in Precision Medicine: Maximum Probability-Driven Reinforcement Learning for Survival Analysis
127. NeurIPS 2025Beyond Scalar Rewards: An Axiomatic Framework for Lexicographic MDPs
128. NeurIPS 2025Boundary-to-Region Supervision for Offline Safe Reinforcement Learning
129. NeurIPS 2025BraVE: Offline Reinforcement Learning for Discrete Combinatorial Action Spaces
130. NeurIPS 2025Breaking the Performance Ceiling in Reinforcement Learning requires Inference Strategies
131. NeurIPS 2025Capturing Individual Human Preferences with Reward Features
132. NeurIPS 2025Certifying Stability of Reinforcement Learning Policies using Generalized Lyapunov Functions
133. NeurIPS 2025COLA: Towards Efficient Multi-Objective Reinforcement Learning with Conflict Objective Regularization in Latent Space
134. NeurIPS 2025Compiler-R1: Towards Agentic Compiler Auto-tuning with Reinforcement Learning
135. NeurIPS 2025Composite Flow Matching for Reinforcement Learning with Shifted-Dynamics Data
136. NeurIPS 2025Computational Hardness of Reinforcement Learning with Partial $q^{\pi}$-Realizability
137. NeurIPS 2025DAIL: Beyond Task Ambiguity for Language-Conditioned Reinforcement Learning
138. NeurIPS 2025DAPO: An Open-Source LLM Reinforcement Learning System at Scale
139. NeurIPS 2025DISCOVER: Automated Curricula for Sparse-Reward Reinforcement Learning
140. NeurIPS 2025Doubly Robust Alignment for Large Language Models
141. NeurIPS 2025Dynamic Configuration for Cutting Plane Separators via Reinforcement Learning on Incremental Graph
142. NeurIPS 2025Efficient Preference-Based Reinforcement Learning: Randomized Exploration meets Experimental Design
143. NeurIPS 2025Eluder dimension: localise it!
144. NeurIPS 2025Enhancing Interpretability in Deep Reinforcement Learning through Semantic Clustering
145. NeurIPS 2025Explainably Safe Reinforcement Learning
146. NeurIPS 2025FANS: A Flatness-Aware Network Structure for Generalization in Offline Reinforcement Learning
147. NeurIPS 2025Finite Sample Analysis of Linear Temporal Difference Learning with Arbitrary Features
148. NeurIPS 2025From Experts to a Generalist: Toward General Whole-Body Control for Humanoid Robots
149. NeurIPS 2025Fuz-RL: A Fuzzy-Guided Robust Framework for Safe Reinforcement Learning under Uncertainty
150. NeurIPS 2025General-Reasoner: Advancing LLM Reasoning Across All Domains

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。