General ML · reinforcement learning papers, 2023–2026

Key findings

• General ML · reinforcement learning went from 287 papers in 2023 to 575 in 2026, roughly 2.0x in three years.
• ICLR publishes the most General ML · reinforcement learning work: 847 papers from 2023 to 2026, 51% of the four-venue total.
• The fastest-growing overlap is with Diffusion Models: papers tagged with both rose from 2 in 2023 to 34 in 2026.
• Among the 6 topics under General Machine Learning, it ranks #2 by 2026 paper count; the fastest grower in the group, General ML · evaluation, is up 4.3x versus 2.0x here.

Representative papers

Spread across years and venues so every edition shows up

• 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

All General ML · reinforcement learning papers

403 papers, page 3 of 9

101. ICLR 2024Making RL with Preference-based Feedback Efficient via Randomization
102. ICLR 2024MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning
103. ICLR 2024Massively Scalable Inverse Reinforcement Learning in Google Maps
104. ICLR 2024Mastering Memory Tasks with World Models
105. ICLR 2024Maximum Entropy Heterogeneous-Agent Reinforcement Learning
106. ICLR 2024Maximum Entropy Model Correction in Reinforcement Learning
107. ICLR 2024Mediator Interpretation and Faster Learning Algorithms for Linear Correlated Equilibria in General Sequential Games
108. ICLR 2024Memory-Consistent Neural Networks for Imitation Learning
109. ICLR 2024Meta Inverse Constrained Reinforcement Learning: Convergence Guarantee and Generalization Analysis
110. ICLR 2024Meta-Evolve: Continuous Robot Evolution for One-to-many Policy Transfer
111. ICLR 2024METRA: Scalable Unsupervised RL with Metric-Aware Abstraction
112. ICLR 2024Motif: Intrinsic Motivation from Artificial Intelligence Feedback
113. ICLR 2024Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts
114. ICLR 2024Negatively Correlated Ensemble Reinforcement Learning for Online Diverse Game Level Generation
115. ICLR 2024ODICE: Revealing the Mystery of Distribution Correction Estimation via Orthogonal-gradient Update
116. ICLR 2024Off-Policy Primal-Dual Safe Reinforcement Learning
117. ICLR 2024Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees
118. ICLR 2024Offline RL with Observation Histories: Analyzing and Improving Sample Complexity
119. ICLR 2024OMNI: Open-endedness via Models of human Notions of Interestingness
120. ICLR 2024On Differentially Private Federated Linear Contextual Bandits
121. ICLR 2024On Double Descent in Reinforcement Learning with LSTD and Random Features
122. ICLR 2024On Stationary Point Convergence of PPO-Clip
123. ICLR 2024On the Expressivity of Objective-Specification Formalisms in Reinforcement Learning
124. ICLR 2024On the Hardness of Constrained Cooperative Multi-Agent Reinforcement Learning
125. ICLR 2024On the Role of General Function Approximation in Offline Reinforcement Learning
126. ICLR 2024On Trajectory Augmentations for Off-Policy Evaluation
127. ICLR 2024Online Information Acquisition: Hiring Multiple Agents
128. ICLR 2024Optimal Sample Complexity for Average Reward Markov Decision Processes
129. ICLR 2024PAE: Reinforcement Learning from External Knowledge for Efficient Exploration
130. ICLR 2024PARL: A Unified Framework for Policy Alignment in Reinforcement Learning from Human Feedback
131. ICLR 2024Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning
132. ICLR 2024Physics-Regulated Deep Reinforcement Learning: Invariant Embeddings
133. ICLR 2024Piecewise Linear Parametrization of Policies: Towards Interpretable Deep Reinforcement Learning
134. ICLR 2024Policy Rehearsing: Training Generalizable Policies for Reinforcement Learning
135. ICLR 2024Pre-Training Goal-based Models for Sample-Efficient Reinforcement Learning
136. ICLR 2024Pre-training with Synthetic Data Helps Offline Reinforcement Learning
137. ICLR 2024Prioritized Soft Q-Decomposition for Lexicographic Reinforcement Learning
138. ICLR 2024Privileged Sensing Scaffolds Reinforcement Learning
139. ICLR 2024Proper Laplacian Representation Learning
140. ICLR 2024Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo
141. ICLR 2024Provable Benefits of Multi-task RL under Non-Markovian Decision Making Processes
142. ICLR 2024Provable Offline Preference-Based Reinforcement Learning
143. ICLR 2024Provable Reward-Agnostic Preference-Based Reinforcement Learning
144. ICLR 2024Provably Efficient CVaR RL in Low-rank MDPs
145. ICLR 2024Provably Efficient Iterated CVaR Reinforcement Learning with Function Approximation and Human Feedback
146. ICLR 2024Provably Efficient UCB-type Algorithms For Learning Predictive State Representations
147. ICLR 2024Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning
148. ICLR 2024Quantifying the Sensitivity of Inverse Reinforcement Learning to Misspecification
149. ICLR 2024Query-Policy Misalignment in Preference-Based Reinforcement Learning
150. ICLR 2024Reasoning with Latent Diffusion in Offline Reinforcement Learning

FAQ

How many General ML · reinforcement learning papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

1,676 in total: 287 in 2023, 403 in 2024, 411 in 2025, 575 in 2026.

How many General ML · reinforcement learning papers are at NeurIPS 2026?

NeurIPS 2026 has 113 General ML · reinforcement learning papers, versus 108 the year before.

How many General ML · reinforcement learning papers are at ICLR 2026?

ICLR 2026 has 335 General ML · reinforcement learning papers, versus 230 the year before.

How many General ML · reinforcement learning papers are at ICML 2026?

ICML 2026 has 113 General ML · reinforcement learning papers, versus 71 the year before.

How many General ML · reinforcement learning papers are at CVPR 2026?

CVPR 2026 has 14 General ML · reinforcement learning papers, versus 2 the year before.

Which conference publishes the most General ML · reinforcement learning papers?

ICLR, with 847 papers from 2023 to 2026.