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 39 of 42

1901. ICML 2023Language Instructed Reinforcement Learning for Human-AI Coordination
1902. ICML 2023Lazy Agents: A New Perspective on Solving Sparse Reward Problem in Multi-agent Reinforcement Learning
1903. ICML 2023Learning Belief Representations for Partially Observable Deep RL
1904. ICML 2023Learning Compiler Pass Orders using Coreset and Normalized Value Prediction
1905. ICML 2023Learning Control-Oriented Dynamical Structure from Data
1906. ICML 2023Learning for Edge-Weighted Online Bipartite Matching with Robustness Guarantees
1907. ICML 2023Learning GFlowNets From Partial Episodes For Improved Convergence And Stability
1908. ICML 2023Learning in POMDPs is Sample-Efficient with Hindsight Observability
1909. ICML 2023Learning Neural Constitutive Laws from Motion Observations for Generalizable PDE Dynamics
1910. ICML 2023Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation
1911. ICML 2023Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise to Noise Mapping
1912. ICML 2023Learning Temporally AbstractWorld Models without Online Experimentation
1913. ICML 2023Learning the Dynamics of Sparsely Observed Interacting Systems
1914. ICML 2023Learning to Design Analog Circuits to Meet Threshold Specifications
1915. ICML 2023Learning to Learn from APIs: Black-Box Data-Free Meta-Learning
1916. ICML 2023Learning to Maximize Mutual Information for Dynamic Feature Selection
1917. ICML 2023Learning to Optimize Differentiable Games
1918. ICML 2023Learning Unforeseen Robustness from Out-of-distribution Data Using Equivariant Domain Translator
1919. ICML 2023Learning useful representations for shifting tasks and distributions
1920. ICML 2023LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option Framework
1921. ICML 2023Leveraging Offline Data in Online Reinforcement Learning
1922. ICML 2023LinSATNet: The Positive Linear Satisfiability Neural Networks
1923. ICML 2023LipsNet: A Smooth and Robust Neural Network with Adaptive Lipschitz Constant for High Accuracy Optimal Control
1924. ICML 2023LIV: Language-Image Representations and Rewards for Robotic Control
1925. ICML 2023Live in the Moment: Learning Dynamics Model Adapted to Evolving Policy
1926. ICML 2023Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning
1927. ICML 2023Low-Switching Policy Gradient with Exploration via Online Sensitivity Sampling
1928. ICML 2023Lower Bounds for Learning in Revealing POMDPs
1929. ICML 2023MAHALO: Unifying Offline Reinforcement Learning and Imitation Learning from Observations
1930. ICML 2023MANSA: Learning Fast and Slow in Multi-Agent Systems
1931. ICML 2023Masked Trajectory Models for Prediction, Representation, and Control
1932. ICML 2023Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels
1933. ICML 2023Meta Optimal Transport
1934. ICML 2023Meta-learning Parameterized Skills
1935. ICML 2023Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization
1936. ICML 2023MetaDiffuser: Diffusion Model as Conditional Planner for Offline Meta-RL
1937. ICML 2023Model Transferability with Responsive Decision Subjects
1938. ICML 2023Model-agnostic Measure of Generalization Difficulty
1939. ICML 2023Model-based Offline Reinforcement Learning with Count-based Conservatism
1940. ICML 2023Model-based Reinforcement Learning with Scalable Composite Policy Gradient Estimators
1941. ICML 2023Model-Bellman Inconsistency for Model-based Offline Reinforcement Learning
1942. ICML 2023Model-Free Robust Average-Reward Reinforcement Learning
1943. ICML 2023Multi-Agent Learning from Learners
1944. ICML 2023Multi-Environment Pretraining Enables Transfer to Action Limited Datasets
1945. ICML 2023Multi-task Hierarchical Adversarial Inverse Reinforcement Learning
1946. ICML 2023Multi-Task Off-Policy Learning from Bandit Feedback
1947. ICML 2023Multi-User Reinforcement Learning with Low Rank Rewards
1948. ICML 2023Multiply Robust Off-policy Evaluation and Learning under Truncation by Death
1949. ICML 2023N$\text{A}^\text{2}$Q: Neural Attention Additive Model for Interpretable Multi-Agent Q-Learning
1950. ICML 2023Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR

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.