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

2,079 papers, page 34 of 42

1651. ICML 2024PlanDQ: Hierarchical Plan Orchestration via D-Conductor and Q-Performer
1652. ICML 2024Planning, Fast and Slow: Online Reinforcement Learning with Action-Free Offline Data via Multiscale Planners
1653. ICML 2024Policy Evaluation for Variance in Average Reward Reinforcement Learning
1654. ICML 2024Policy Learning for Balancing Short-Term and Long-Term Rewards
1655. ICML 2024Policy-conditioned Environment Models are More Generalizable
1656. ICML 2024Position: Amazing Things Come From Having Many Good Models
1657. ICML 2024Position: Automatic Environment Shaping is the Next Frontier in RL
1658. ICML 2024Position: Benchmarking is Limited in Reinforcement Learning Research
1659. ICML 2024Position: Insights from Survey Methodology can Improve Training Data
1660. ICML 2024Position: Leverage Foundational Models for Black-Box Optimization
1661. ICML 2024Position: Reinforcement Learning in Dynamic Treatment Regimes Needs Critical Reexamination
1662. ICML 2024Position: Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback
1663. ICML 2024Position: Social Environment Design Should be Further Developed for AI-based Policy-Making
1664. ICML 2024Position: Video as the New Language for Real-World Decision Making
1665. ICML 2024Predictive Performance Comparison of Decision Policies Under Confounding
1666. ICML 2024Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data
1667. ICML 2024Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF
1668. ICML 2024Privacy-Preserving Instructions for Aligning Large Language Models
1669. ICML 2024Probabilistic Constrained Reinforcement Learning with Formal Interpretability
1670. ICML 2024Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo
1671. ICML 2024Probabilistic Subgoal Representations for Hierarchical Reinforcement Learning
1672. ICML 2024Profile Reconstruction from Private Sketches
1673. ICML 2024Prompt-based Visual Alignment for Zero-shot Policy Transfer
1674. ICML 2024Provable Representation with Efficient Planning for Partially Observable Reinforcement Learning
1675. ICML 2024Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation
1676. ICML 2024Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret
1677. ICML 2024Provably Efficient Partially Observable Risk-sensitive Reinforcement Learning with Hindsight Observation
1678. ICML 2024Provably Efficient Reinforcement Learning for Adversarial Restless Multi-Armed Bandits with Unknown Transitions and Bandit Feedback
1679. ICML 2024Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgent
1680. ICML 2024Q-value Regularized Transformer for Offline Reinforcement Learning
1681. ICML 2024QORA: Zero-Shot Transfer via Interpretable Object-Relational Model Learning
1682. ICML 2024Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven Optimization
1683. ICML 2024Quality-Diversity Actor-Critic: Learning High-Performing and Diverse Behaviors via Value and Successor Features Critics
1684. ICML 2024Quality-Diversity with Limited Resources
1685. ICML 2024Quality-Weighted Vendi Scores And Their Application To Diverse Experimental Design
1686. ICML 2024Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency
1687. ICML 2024Random Latent Exploration for Deep Reinforcement Learning
1688. ICML 2024Rate-Optimal Policy Optimization for Linear Markov Decision Processes
1689. ICML 2024Recovering Labels from Local Updates in Federated Learning
1690. ICML 2024ReDiffuser: Reliable Decision-Making Using a Diffuser with Confidence Estimation
1691. ICML 2024Refining Minimax Regret for Unsupervised Environment Design
1692. ICML 2024Reflective Policy Optimization
1693. ICML 2024Reinforcement Learning and Regret Bounds for Admission Control
1694. ICML 2024Reinforcement Learning from Reachability Specifications: PAC Guarantees with Expected Conditional Distance
1695. ICML 2024Reinforcement Learning within Tree Search for Fast Macro Placement
1696. ICML 2024Reinformer: Max-Return Sequence Modeling for Offline RL
1697. ICML 2024ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages
1698. ICML 2024ReMax: A Simple, Effective, and Efficient Reinforcement Learning Method for Aligning Large Language Models
1699. ICML 2024Remembering to Be Fair: Non-Markovian Fairness in Sequential Decision Making
1700. ICML 2024Removing Spurious Concepts from Neural Network Representations via Joint Subspace Estimation

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.