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

468 papers, page 4 of 10

151. NeurIPS 2025Global Convergence for Average Reward Constrained MDPs with Primal-Dual Actor Critic Algorithm
152. NeurIPS 2025Greedy Sampling Is Provably Efficient For RLHF
153. NeurIPS 2025Gymnasium: A Standard Interface for Reinforcement Learning Environments
154. NeurIPS 2025Horizon Reduction Makes RL Scalable
155. NeurIPS 2025How Ensembles of Distilled Policies Improve Generalisation in Reinforcement Learning
156. NeurIPS 2025Imagined Autocurricula
157. NeurIPS 2025Imitation Beyond Expectation Using Pluralistic Stochastic Dominance
158. NeurIPS 2025Imitation Learning with Temporal Logic Constraints
159. NeurIPS 2025Improving Model-Based Reinforcement Learning by Converging to Flatter Minima
160. NeurIPS 2025Improving Reward Models with Proximal Policy Exploration for Preference-Based Reinforcement Learning
161. NeurIPS 2025Intrinsic Benefits of Categorical Distributional Loss: Uncertainty-aware Regularized Exploration in Reinforcement Learning
162. NeurIPS 2025Learning Parameterized Skills from Demonstrations
163. NeurIPS 2025Learning Personalized Ad Impact via Contextual Reinforcement Learning under Delayed Rewards
164. NeurIPS 2025Learning to Clean: Reinforcement Learning for Noisy Label Correction
165. NeurIPS 2025Measure gradients, not activations! Enhancing neuronal activity in deep reinforcement learning
166. NeurIPS 2025Meta-learning how to Share Credit among Macro-Actions
167. NeurIPS 2025Mind the GAP! The Challenges of Scale in Pixel-based Deep Reinforcement Learning
168. NeurIPS 2025Mixture-of-Experts Meets In-Context Reinforcement Learning
169. NeurIPS 2025Novel Exploration via Orthogonality
170. NeurIPS 2025Offline Actor-Critic for Average Reward MDPs
171. NeurIPS 2025Offline Goal-conditioned Reinforcement Learning with Quasimetric Representations
172. NeurIPS 2025Offline imitation learning in $Q^\pi$-realizable MDPs without expert realizability
173. NeurIPS 2025Omni-R1: Reinforcement Learning for Omnimodal Reasoning via Two-System Collaboration
174. NeurIPS 2025On the Global Optimality of Policy Gradient Methods in General Utility Reinforcement Learning
175. NeurIPS 2025On the Sample Complexity Bounds of Bilevel Reinforcement Learning
176. NeurIPS 2025Optimal Dynamic Regret by Transformers for Non-Stationary Reinforcement Learning
177. NeurIPS 2025Optimal Regret Bounds via Low-Rank Structured Variation in Non-Stationary Reinforcement Learning
178. NeurIPS 2025Option-aware Temporally Abstracted Value for Offline Goal-Conditioned Reinforcement Learning
179. NeurIPS 2025Perception-R1: Pioneering Perception Policy with Reinforcement Learning
180. NeurIPS 2025Preference Distillation via Value based Reinforcement Learning
181. NeurIPS 2025Preference-based Reinforcement Learning beyond Pairwise Comparisons: Benefits of Multiple Options
182. NeurIPS 2025Pretraining a Shared Q-Network for Data-Efficient Offline Reinforcement Learning
183. NeurIPS 2025Progress Reward Model for Reinforcement Learning via Large Language Models
184. NeurIPS 2025Quantization-Free Autoregressive Action Transformer
185. NeurIPS 2025Real-World Reinforcement Learning of Active Perception Behaviors
186. NeurIPS 2025REINFORCE Converges to Optimal Policies with Any Learning Rate
187. NeurIPS 2025Reinforcement Learning Finetunes Small Subnetworks in Large Language Models
188. NeurIPS 2025Reinforcement Learning with Backtracking Feedback
189. NeurIPS 2025Reinforcement Learning with Imperfect Transition Predictions: A Bellman-Jensen Approach
190. NeurIPS 2025Reward-Aware Proto-Representations in Reinforcement Learning
191. NeurIPS 2025Risk-Averse Constrained Reinforcement Learning with Optimized Certainty Equivalents
192. NeurIPS 2025Risk-Averse Total-Reward Reinforcement Learning
193. NeurIPS 2025RLVR-World: Training World Models with Reinforcement Learning
194. NeurIPS 2025RULE: Reinforcement UnLEarning Achieves Forget-retain Pareto Optimality
195. NeurIPS 2025Sample Complexity of Distributionally Robust Average-Reward Reinforcement Learning
196. NeurIPS 2025SeRL: Self-play Reinforcement Learning for Large Language Models with Limited Data
197. NeurIPS 2025Shift Before You Learn: Enabling Low-Rank Representations in Reinforcement Learning
198. NeurIPS 2025ShiQ: Bringing back Bellman to LLMs
199. NeurIPS 2025Skill-Driven Neurosymbolic State Abstractions
200. NeurIPS 2025Solving Continuous Mean Field Games: Deep Reinforcement Learning for Non-Stationary Dynamics

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.