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 127 of 138

6301. ICML 2023Policy Gradient in Robust MDPs with Global Convergence Guarantee
6302. ICML 2023Policy Regularization with Dataset Constraint for Offline Reinforcement Learning
6303. ICML 2023Posterior Sampling for Deep Reinforcement Learning
6304. ICML 2023PPG Reloaded: An Empirical Study on What Matters in Phasic Policy Gradient
6305. ICML 2023Predictable MDP Abstraction for Unsupervised Model-Based RL
6306. ICML 2023Predicting Rare Events by Shrinking Towards Proportional Odds
6307. ICML 2023Principled Offline RL in the Presence of Rich Exogenous Information
6308. ICML 2023Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons
6309. ICML 2023Prometheus: Taming Sample and Communication Complexities in Constrained Decentralized Stochastic Bilevel Learning
6310. ICML 2023Provable Benefit of Mixup for Finding Optimal Decision Boundaries
6311. ICML 2023Provable Reset-free Reinforcement Learning by No-Regret Reduction
6312. ICML 2023Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources
6313. ICML 2023Provably Invariant Learning without Domain Information
6314. ICML 2023Q-learning Decision Transformer: Leveraging Dynamic Programming for Conditional Sequence Modelling in Offline RL
6315. ICML 2023QAS-Bench: Rethinking Quantum Architecture Search and A Benchmark
6316. ICML 2023Quantile Credit Assignment
6317. ICML 2023Quantum Policy Gradient Algorithm with Optimized Action Decoding
6318. ICML 2023RACE: Improve Multi-Agent Reinforcement Learning with Representation Asymmetry and Collaborative Evolution
6319. ICML 2023Reachability-Aware Laplacian Representation in Reinforcement Learning
6320. ICML 2023Regret Bounds for Markov Decision Processes with Recursive Optimized Certainty Equivalents
6321. ICML 2023Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice
6322. ICML 2023Reinforcement Learning Can Be More Efficient with Multiple Rewards
6323. ICML 2023Reinforcement Learning from Passive Data via Latent Intentions
6324. ICML 2023Reinforcement Learning in Low-rank MDPs with Density Features
6325. ICML 2023Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space
6326. ICML 2023Reinforcement Learning with History Dependent Dynamic Contexts
6327. ICML 2023ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs
6328. ICML 2023Reparameterized Policy Learning for Multimodal Trajectory Optimization
6329. ICML 2023Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL
6330. ICML 2023Representation-Driven Reinforcement Learning
6331. ICML 2023Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition
6332. ICML 2023Retrosynthetic Planning with Dual Value Networks
6333. ICML 2023Revisiting Bellman Errors for Offline Model Selection
6334. ICML 2023Revisiting Domain Randomization via Relaxed State-Adversarial Policy Optimization
6335. ICML 2023Revisiting the Linear-Programming Framework for Offline RL with General Function Approximation
6336. ICML 2023Reward-Mixing MDPs with Few Latent Contexts are Learnable
6337. ICML 2023RLang: A Declarative Language for Describing Partial World Knowledge to Reinforcement Learning Agents
6338. ICML 2023Robust Collaborative Learning with Linear Gradient Overhead
6339. ICML 2023Robust Satisficing MDPs
6340. ICML 2023Robust Situational Reinforcement Learning in Face of Context Disturbances
6341. ICML 2023Robust Subtask Learning for Compositional Generalization
6342. ICML 2023Robustly Learning a Single Neuron via Sharpness
6343. ICML 2023Safe Offline Reinforcement Learning with Real-Time Budget Constraints
6344. ICML 2023SAM operates far from home: eigenvalue regularization as a dynamical phenomenon
6345. ICML 2023Sample Complexity Bounds for Learning High-dimensional Simplices in Noisy Regimes
6346. ICML 2023Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation
6347. ICML 2023Scalable Safe Policy Improvement via Monte Carlo Tree Search
6348. ICML 2023Scaling Laws for Reward Model Overoptimization
6349. ICML 2023Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning
6350. ICML 2023SeMAIL: Eliminating Distractors in Visual Imitation via Separated Models

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.