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

1,070 papers, page 12 of 22

551. ICML 2023Supported Trust Region Optimization for Offline Reinforcement Learning
552. ICML 2023Target-based Surrogates for Stochastic Optimization
553. ICML 2023TGRL: An Algorithm for Teacher Guided Reinforcement Learning
554. ICML 2023The Benefits of Model-Based Generalization in Reinforcement Learning
555. ICML 2023The Dormant Neuron Phenomenon in Deep Reinforcement Learning
556. ICML 2023The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation
557. ICML 2023The Regret of Exploration and the Control of Bad Episodes in Reinforcement Learning
558. ICML 2023The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation
559. ICML 2023The Unintended Consequences of Discount Regularization: Improving Regularization in Certainty Equivalence Reinforcement Learning
560. ICML 2023The Virtues of Laziness in Model-based RL: A Unified Objective and Algorithms
561. ICML 2023The Wisdom of Hindsight Makes Language Models Better Instruction Followers
562. ICML 2023Tighter Information-Theoretic Generalization Bounds from Supersamples
563. ICML 2023Toward Efficient Gradient-Based Value Estimation
564. ICML 2023Towards a better understanding of representation dynamics under TD-learning
565. ICML 2023Towards Learning to Imitate from a Single Video Demonstration
566. ICML 2023Towards Robust and Safe Reinforcement Learning with Benign Off-policy Data
567. ICML 2023Towards Theoretical Understanding of Inverse Reinforcement Learning
568. ICML 2023Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning
569. ICML 2023Truncating Trajectories in Monte Carlo Reinforcement Learning
570. ICML 2023Tuning Computer Vision Models With Task Rewards
571. ICML 2023Two Losses Are Better Than One: Faster Optimization Using a Cheaper Proxy
572. ICML 2023Understanding and Generalizing Contrastive Learning from the Inverse Optimal Transport Perspective
573. ICML 2023Understanding Plasticity in Neural Networks
574. ICML 2023Understanding Self-Predictive Learning for Reinforcement Learning
575. ICML 2023Understanding the Complexity Gains of Single-Task RL with a Curriculum
576. ICML 2023Universal Morphology Control via Contextual Modulation
577. ICML 2023VA-learning as a more efficient alternative to Q-learning
578. ICML 2023Variance Control for Distributional Reinforcement Learning
579. ICML 2023Variational Curriculum Reinforcement Learning for Unsupervised Discovery of Skills
580. ICML 2023VectorMapNet: End-to-end Vectorized HD Map Learning
581. ICML 2023Warm-Start Actor-Critic: From Approximation Error to Sub-optimality Gap
582. ICML 2023Weighted Sampling without Replacement for Deep Top-$k$ Classification
583. ICML 2023What can online reinforcement learning with function approximation benefit from general coverage conditions?
584. ICML 2023What is Essential for Unseen Goal Generalization of Offline Goal-conditioned RL?
585. ICML 2023When is Realizability Sufficient for Off-Policy Reinforcement Learning?
586. ICML 2023Why Target Networks Stabilise Temporal Difference Methods
587. NeurIPS 2023“Why Not Looking backward?” A Robust Two-Step Method to Automatically Terminate Bayesian Optimization
588. NeurIPS 2023$\texttt{TACO}$: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement Learning
589. NeurIPS 2023A Definition of Continual Reinforcement Learning
590. NeurIPS 2023A Long $N$-step Surrogate Stage Reward for Deep Reinforcement Learning
591. NeurIPS 2023A Novel Framework for Policy Mirror Descent with General Parameterization and Linear Convergence
592. NeurIPS 2023A Partially-Supervised Reinforcement Learning Framework for Visual Active Search
593. NeurIPS 2023A Reduction-based Framework for Sequential Decision Making with Delayed Feedback
594. NeurIPS 2023A Robust and Opponent-Aware League Training Method for StarCraft II
595. NeurIPS 2023A Simple Solution for Offline Imitation from Observations and Examples with Possibly Incomplete Trajectories
596. NeurIPS 2023A State Representation for Diminishing Rewards
597. NeurIPS 2023A Theoretical Analysis of Optimistic Proximal Policy Optimization in Linear Markov Decision Processes
598. NeurIPS 2023A Toolkit for Reliable Benchmarking and Research in Multi-Objective Reinforcement Learning
599. NeurIPS 2023A Trichotomy for Transductive Online Learning
600. NeurIPS 2023A Unified Algorithm Framework for Unsupervised Discovery of Skills based on Determinantal Point Process

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.