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 136 of 138

6751. NeurIPS 2023Recovering from Out-of-sample States via Inverse Dynamics in Offline Reinforcement Learning
6752. NeurIPS 2023Recurrent Hypernetworks are Surprisingly Strong in Meta-RL
6753. NeurIPS 2023ReDS: Offline RL With Heteroskedastic Datasets via Support Constraints
6754. NeurIPS 2023Reduced Policy Optimization for Continuous Control with Hard Constraints
6755. NeurIPS 2023Reducing Blackwell and Average Optimality to Discounted MDPs via the Blackwell Discount Factor
6756. NeurIPS 2023Refining Diffusion Planner for Reliable Behavior Synthesis by Automatic Detection of Infeasible Plans
6757. NeurIPS 2023Reflexion: language agents with verbal reinforcement learning
6758. NeurIPS 2023Regret Minimization via Saddle Point Optimization
6759. NeurIPS 2023Regret-Optimal Model-Free Reinforcement Learning for Discounted MDPs with Short Burn-In Time
6760. NeurIPS 2023Regularity as Intrinsic Reward for Free Play
6761. NeurIPS 2023Regularized Behavior Cloning for Blocking the Leakage of Past Action Information
6762. NeurIPS 2023Reinforcement Learning with Fast and Forgetful Memory
6763. NeurIPS 2023Reinforcement Learning with Simple Sequence Priors
6764. NeurIPS 2023Reinforcement-Enhanced Autoregressive Feature Transformation: Gradient-steered Search in Continuous Space for Postfix Expressions
6765. NeurIPS 2023Reining Generalization in Offline Reinforcement Learning via Representation Distinction
6766. NeurIPS 2023Replicability in Reinforcement Learning
6767. NeurIPS 2023Replicable Reinforcement Learning
6768. NeurIPS 2023RePo: Resilient Model-Based Reinforcement Learning by Regularizing Posterior Predictability
6769. NeurIPS 2023Resetting the Optimizer in Deep RL: An Empirical Study
6770. NeurIPS 2023Residual Q-Learning: Offline and Online Policy Customization without Value
6771. NeurIPS 2023Revisiting Implicit Differentiation for Learning Problems in Optimal Control
6772. NeurIPS 2023Revisiting the Minimalist Approach to Offline Reinforcement Learning
6773. NeurIPS 2023Reward Finetuning for Faster and More Accurate Unsupervised Object Discovery
6774. NeurIPS 2023Reward Scale Robustness for Proximal Policy Optimization via DreamerV3 Tricks
6775. NeurIPS 2023Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning
6776. NeurIPS 2023Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement
6777. NeurIPS 2023Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards
6778. NeurIPS 2023Rewiring Neurons in Non-Stationary Environments
6779. NeurIPS 2023Risk-Averse Active Sensing for Timely Outcome Prediction under Cost Pressure
6780. NeurIPS 2023Risk-Averse Model Uncertainty for Distributionally Robust Safe Reinforcement Learning
6781. NeurIPS 2023RiskQ: Risk-sensitive Multi-Agent Reinforcement Learning Value Factorization
6782. NeurIPS 2023RL-based Stateful Neural Adaptive Sampling and Denoising for Real-Time Path Tracing
6783. NeurIPS 2023RL-ViGen: A Reinforcement Learning Benchmark for Visual Generalization
6784. NeurIPS 2023RoboCLIP: One Demonstration is Enough to Learn Robot Policies
6785. NeurIPS 2023RoboHive: A Unified Framework for Robot Learning
6786. NeurIPS 2023Robust Knowledge Transfer in Tiered Reinforcement Learning
6787. NeurIPS 2023Robust Multi-Agent Reinforcement Learning via Adversarial Regularization: Theoretical Foundation and Stable Algorithms
6788. NeurIPS 2023Safe Exploration in Reinforcement Learning: A Generalized Formulation and Algorithms
6789. NeurIPS 2023SafeDICE: Offline Safe Imitation Learning with Non-Preferred Demonstrations
6790. NeurIPS 2023Safety Gymnasium: A Unified Safe Reinforcement Learning Benchmark
6791. NeurIPS 2023Safety Verification of Decision-Tree Policies in Continuous Time
6792. NeurIPS 2023Sample Complexity for Quadratic Bandits: Hessian Dependent Bounds and Optimal Algorithms
6793. NeurIPS 2023Sample Complexity of Goal-Conditioned Hierarchical Reinforcement Learning
6794. NeurIPS 2023Sample Efficient Reinforcement Learning in Mixed Systems through Augmented Samples and Its Applications to Queueing Networks
6795. NeurIPS 2023Sample-Efficient and Safe Deep Reinforcement Learning via Reset Deep Ensemble Agents
6796. NeurIPS 2023Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
6797. NeurIPS 2023SARAMIS: Simulation Assets for Robotic Assisted and Minimally Invasive Surgery
6798. NeurIPS 2023Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities
6799. NeurIPS 2023Scalarization for Multi-Task and Multi-Domain Learning at Scale
6800. NeurIPS 2023ScenarioNet: Open-Source Platform for Large-Scale Traffic Scenario Simulation and Modeling

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.