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

6,885 papers, page 124 of 138

6151. ICML 2023ContraBAR: Contrastive Bayes-Adaptive Deep RL
6152. ICML 2023Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learning
6153. ICML 2023Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation
6154. ICML 2023Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes
6155. ICML 2023Curious Replay for Model-based Adaptation
6156. ICML 2023Decentralized Stochastic Bilevel Optimization with Improved per-Iteration Complexity
6157. ICML 2023Deep Laplacian-based Options for Temporally-Extended Exploration
6158. ICML 2023Delay-Adapted Policy Optimization and Improved Regret for Adversarial MDP with Delayed Bandit Feedback
6159. ICML 2023Demonstration-free Autonomous Reinforcement Learning via Implicit and Bidirectional Curriculum
6160. ICML 2023Detecting Adversarial Directions in Deep Reinforcement Learning to Make Robust Decisions
6161. ICML 2023Differentiable and Transportable Structure Learning
6162. ICML 2023Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards
6163. ICML 2023Discovering Object-Centric Generalized Value Functions From Pixels
6164. ICML 2023Distance Weighted Supervised Learning for Offline Interaction Data
6165. ICML 2023Distributional Offline Policy Evaluation with Predictive Error Guarantees
6166. ICML 2023Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making using Language Guided World Modelling
6167. ICML 2023Does Sparsity Help in Learning Misspecified Linear Bandits?
6168. ICML 2023Dynamical Linear Bandits
6169. ICML 2023ED-Batch: Efficient Automatic Batching of Dynamic Neural Networks via Learned Finite State Machines
6170. ICML 2023Efficient Exploration via Epistemic-Risk-Seeking Policy Optimization
6171. ICML 2023Efficient Online Reinforcement Learning with Offline Data
6172. ICML 2023Emergence of Adaptive Circadian Rhythms in Deep Reinforcement Learning
6173. ICML 2023Emergence of Sparse Representations from Noise
6174. ICML 2023Emergent Agentic Transformer from Chain of Hindsight Experience
6175. ICML 2023Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments
6176. ICML 2023Entity Divider with Language Grounding in Multi-Agent Reinforcement Learning
6177. ICML 2023Estimating Heterogeneous Treatment Effects: Mutual Information Bounds and Learning Algorithms
6178. ICML 2023Estimation Beyond Data Reweighting: Kernel Method of Moments
6179. ICML 2023Explainable Data-Driven Optimization: From Context to Decision and Back Again
6180. ICML 2023Explaining Reinforcement Learning with Shapley Values
6181. ICML 2023Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning
6182. ICML 2023Fair Densities via Boosting the Sufficient Statistics of Exponential Families
6183. ICML 2023Fair yet Asymptotically Equal Collaborative Learning
6184. ICML 2023Faith-Shap: The Faithful Shapley Interaction Index
6185. ICML 2023Fast Rates for Maximum Entropy Exploration
6186. ICML 2023Federated Online and Bandit Convex Optimization
6187. ICML 2023FLEX: an Adaptive Exploration Algorithm for Nonlinear Systems
6188. ICML 2023Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement Learning
6189. ICML 2023For Pre-Trained Vision Models in Motor Control, Not All Policy Learning Methods are Created Equal
6190. ICML 2023FREDIS: A Fusion Framework of Refinement and Disambiguation for Unreliable Partial Label Learning
6191. ICML 2023From Hypergraph Energy Functions to Hypergraph Neural Networks
6192. ICML 2023From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning
6193. ICML 2023Fundamental Tradeoffs in Learning with Prior Information
6194. ICML 2023Future-conditioned Unsupervised Pretraining for Decision Transformer
6195. ICML 2023GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Models
6196. ICML 2023Go Beyond Imagination: Maximizing Episodic Reachability with World Models
6197. ICML 2023Graph Reinforcement Learning for Network Control via Bi-Level Optimization
6198. ICML 2023Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning
6199. ICML 2023Guiding Pretraining in Reinforcement Learning with Large Language Models
6200. ICML 2023Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games

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.