Reinforcement Learning papers, 2023–2026

Key findings

• Reinforcement Learning went from 1,070 papers in 2023 to 2,985 in 2026, roughly 2.8x in three years.
• NeurIPS publishes the most Reinforcement Learning work: 2,494 papers from 2023 to 2026, 36% of the four-venue total.
• The fastest-growing overlap is with Reasoning Models: papers tagged with both rose from 9 in 2023 to 437 in 2026.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026\texttt{FEROM}: Frontier Endogenous Reveal-Order Marginal Policy Optimization for Masked Diffusion LMs
• ICML 2026$\text{DT}^\text{2}$: Decision-Targeted Digital Twins
• ICLR 2026$\nabla$-Reasoner: LLM Reasoning via Test-Time Gradient Descent in Textual Space
• CVPR 2026A Causal Marriage between VLM and IRM from Understanding to
• NeurIPS 2025$\texttt{G1}$: Teaching LLMs to Reason on Graphs with Reinforcement Learning
• ICML 2025A Computationally Efficient Algorithm for Infinite-Horizon Average-Reward Linear MDPs
• ICLR 2025$\phi$-Update: A Class of Policy Update Methods with Policy Convergence Guarantee
• CVPR 2025All-Optical Nonlinear Diffractive Deep Network for Ultrafast Image Denoising
• NeurIPS 2024A Critical Evaluation of AI Feedback for Aligning Large Language Models
• ICML 2024${\rm E}(3)$-Equivariant Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning
• ICLR 2024A Black-box Approach for Non-stationary Multi-agent Reinforcement Learning
• CVPR 2024DMR: Decomposed Multi-Modality Representations for Frames and Events Fusion in Visual Reinforcement Learning
• NeurIPS 2023“Why Not Looking backward?” A Robust Two-Step Method to Automatically Terminate Bayesian Optimization
• ICML 2023A Connection between One-Step RL and Critic Regularization in Reinforcement Learning
• ICLR 2023$O(T^{-1})$ Convergence of Optimistic-Follow-the-Regularized-Leader in Two-Player Zero-Sum Markov Games
• CVPR 2023"Seeing" Electric Network Frequency From Events
• NeurIPS 2026$\varphi$TD: Distributional Reinforcement Learning using Characteristic Functions
• ICML 2026$\textit{S}$-SPPO: Semantic-Calibrated Self-Play Preference Optimization
• ICLR 2026$AutoDrive\text{-}P^3$: Unified Chain of Perception–Prediction–Planning Thought via Reinforcement Fine-Tuning
• CVPR 2026AdaptVision Efficient Vision-Language Models via Adaptive Visual Acquisition

All Reinforcement Learning papers

356 papers, page 5 of 8

201. ICML 2024Offline Actor-Critic Reinforcement Learning Scales to Large Models
202. ICML 2024Offline Imitation from Observation via Primal Wasserstein State Occupancy Matching
203. ICML 2024Offline Inverse RL: New Solution Concepts and Provably Efficient Algorithms
204. ICML 2024Offline Multi-Objective Optimization
205. ICML 2024Offline Transition Modeling via Contrastive Energy Learning
206. ICML 2024Offline-Boosted Actor-Critic: Adaptively Blending Optimal Historical Behaviors in Deep Off-Policy RL
207. ICML 2024OLLIE: Imitation Learning from Offline Pretraining to Online Finetuning
208. ICML 2024OMPO: A Unified Framework for RL under Policy and Dynamics Shifts
209. ICML 2024On the Second-Order Convergence of Biased Policy Gradient Algorithms
210. ICML 2024On The Statistical Complexity of Offline Decision-Making
211. ICML 2024On the Unexpected Effectiveness of Reinforcement Learning for Sequential Recommendation
212. ICML 2024On Which Nodes Does GCN Fail? Enhancing GCN From the Node Perspective
213. ICML 2024Online Matching with Stochastic Rewards: Provable Better Bound via Adversarial Reinforcement Learning
214. ICML 2024Optimal Hessian/Jacobian-Free Nonconvex-PL Bilevel Optimization
215. ICML 2024OSN: Infinite Representations of Dynamic 3D Scenes from Monocular Videos
216. ICML 2024Overcoming the Optimizer's Curse: Obtaining Realistic Prescriptions from Neural Networks
217. ICML 2024Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning
218. ICML 2024Parameter Estimation in DAGs from Incomplete Data via Optimal Transport
219. ICML 2024Pausing Policy Learning in Non-stationary Reinforcement Learning
220. ICML 2024PEARL: Zero-shot Cross-task Preference Alignment and Robust Reward Learning for Robotic Manipulation
221. ICML 2024Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning
222. ICML 2024Pi-DUAL: Using privileged information to distinguish clean from noisy labels
223. ICML 2024PICLe: Eliciting Diverse Behaviors from Large Language Models with Persona In-Context Learning
224. ICML 2024PIPER: Primitive-Informed Preference-based Hierarchical Reinforcement Learning via Hindsight Relabeling
225. ICML 2024PlanDQ: Hierarchical Plan Orchestration via D-Conductor and Q-Performer
226. ICML 2024Planning, Fast and Slow: Online Reinforcement Learning with Action-Free Offline Data via Multiscale Planners
227. ICML 2024Policy Evaluation for Variance in Average Reward Reinforcement Learning
228. ICML 2024Policy Learning for Balancing Short-Term and Long-Term Rewards
229. ICML 2024Policy-conditioned Environment Models are More Generalizable
230. ICML 2024Position: Amazing Things Come From Having Many Good Models
231. ICML 2024Position: Automatic Environment Shaping is the Next Frontier in RL
232. ICML 2024Position: Benchmarking is Limited in Reinforcement Learning Research
233. ICML 2024Position: Insights from Survey Methodology can Improve Training Data
234. ICML 2024Position: Leverage Foundational Models for Black-Box Optimization
235. ICML 2024Position: Reinforcement Learning in Dynamic Treatment Regimes Needs Critical Reexamination
236. ICML 2024Position: Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback
237. ICML 2024Position: Social Environment Design Should be Further Developed for AI-based Policy-Making
238. ICML 2024Position: Video as the New Language for Real-World Decision Making
239. ICML 2024Predictive Performance Comparison of Decision Policies Under Confounding
240. ICML 2024Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data
241. ICML 2024Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF
242. ICML 2024Privacy-Preserving Instructions for Aligning Large Language Models
243. ICML 2024Probabilistic Constrained Reinforcement Learning with Formal Interpretability
244. ICML 2024Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo
245. ICML 2024Probabilistic Subgoal Representations for Hierarchical Reinforcement Learning
246. ICML 2024Profile Reconstruction from Private Sketches
247. ICML 2024Prompt-based Visual Alignment for Zero-shot Policy Transfer
248. ICML 2024Provable Representation with Efficient Planning for Partially Observable Reinforcement Learning
249. ICML 2024Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation
250. ICML 2024Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret

FAQ

How many Reinforcement Learning papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

6,885 in total: 1,070 in 2023, 1,190 in 2024, 1,640 in 2025, 2,985 in 2026.

How many Reinforcement Learning papers are at NeurIPS 2026?

NeurIPS 2026 has 760 Reinforcement Learning papers, versus 784 the year before.

How many Reinforcement Learning papers are at ICLR 2026?

ICLR 2026 has 911 Reinforcement Learning papers, versus 379 the year before.

How many Reinforcement Learning papers are at ICML 2026?

ICML 2026 has 1,021 Reinforcement Learning papers, versus 405 the year before.

How many Reinforcement Learning papers are at CVPR 2026?

CVPR 2026 has 293 Reinforcement Learning papers, versus 72 the year before.

Which conference publishes the most Reinforcement Learning papers?

NeurIPS, with 2,494 papers from 2023 to 2026.