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

356 papers, page 2 of 8

51. ICML 2024Cross-Domain Policy Adaptation by Capturing Representation Mismatch
52. ICML 2024Dealing With Unbounded Gradients in Stochastic Saddle-point Optimization
53. ICML 2024Debiased Offline Representation Learning for Fast Online Adaptation in Non-stationary Dynamics
54. ICML 2024Decoding-time Realignment of Language Models
55. ICML 2024Decoupling Learning and Decision-Making: Breaking the $\mathcal{O}(\sqrt{T})$ Barrier in Online Resource Allocation with First-Order Methods
56. ICML 2024Deep Demonstration Tracing: Learning Generalizable Imitator Policy for Runtime Imitation from a Single Demonstration
57. ICML 2024Degeneration-free Policy Optimization: RL Fine-Tuning for Language Models without Degeneration
58. ICML 2024Dense Reward for Free in Reinforcement Learning from Human Feedback
59. ICML 2024Detecting Influence Structures in Multi-Agent Reinforcement Learning
60. ICML 2024DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching
61. ICML 2024Discovering Features with Synergistic Interactions in Multiple Views
62. ICML 2024Discovering Multiple Solutions from a Single Task in Offline Reinforcement Learning
63. ICML 2024Distinguishing the Knowable from the Unknowable with Language Models
64. ICML 2024Distributional Bellman Operators over Mean Embeddings
65. ICML 2024Do Transformer World Models Give Better Policy Gradients?
66. ICML 2024DPN: Decoupling Partition and Navigation for Neural Solvers of Min-max Vehicle Routing Problems
67. ICML 2024Dr. Strategy: Model-Based Generalist Agents with Strategic Dreaming
68. ICML 2024DRED: Zero-Shot Transfer in Reinforcement Learning via Data-Regularised Environment Design
69. ICML 2024DynSyn: Dynamical Synergistic Representation for Efficient Learning and Control in Overactuated Embodied Systems
70. ICML 2024ED-Copilot: Reduce Emergency Department Wait Time with Language Model Diagnostic Assistance
71. ICML 2024Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning
72. ICML 2024Efficient Exploration in Average-Reward Constrained Reinforcement Learning: Achieving Near-Optimal Regret With Posterior Sampling
73. ICML 2024Efficient Policy Evaluation with Offline Data Informed Behavior Policy Design
74. ICML 2024Efficient World Models with Context-Aware Tokenization
75. ICML 2024EfficientZero V2: Mastering Discrete and Continuous Control with Limited Data
76. ICML 2024Emergence of In-Context Reinforcement Learning from Noise Distillation
77. ICML 2024End-to-End Neuro-Symbolic Reinforcement Learning with Textual Explanations
78. ICML 2024Energy-Guided Diffusion Sampling for Offline-to-Online Reinforcement Learning
79. ICML 2024Enhancing Value Function Estimation through First-Order State-Action Dynamics in Offline Reinforcement Learning
80. ICML 2024Environment Design for Inverse Reinforcement Learning
81. ICML 2024EvIL: Evolution Strategies for Generalisable Imitation Learning
82. ICML 2024EvoRainbow: Combining Improvements in Evolutionary Reinforcement Learning for Policy Search
83. ICML 2024Exploration and Anti-Exploration with Distributional Random Network Distillation
84. ICML 2024Exploration-Driven Policy Optimization in RLHF: Theoretical Insights on Efficient Data Utilization
85. ICML 2024Exploring the LLM Journey from Cognition to Expression with Linear Representations
86. ICML 2024Factored-Reward Bandits with Intermediate Observations
87. ICML 2024Failures Are Fated, But Can Be Faded: Characterizing and Mitigating Unwanted Behaviors in Large-Scale Vision and Language Models
88. ICML 2024Fair Off-Policy Learning from Observational Data
89. ICML 2024Fair Resource Allocation in Multi-Task Learning
90. ICML 2024Fast Decision Boundary based Out-of-Distribution Detector
91. ICML 2024Feasibility Consistent Representation Learning for Safe Reinforcement Learning
92. ICML 2024Feasible Reachable Policy Iteration
93. ICML 2024Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices
94. ICML 2024Feedback Efficient Online Fine-Tuning of Diffusion Models
95. ICML 2024FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning
96. ICML 2024Fine-Grained Causal Dynamics Learning with Quantization for Improving Robustness in Reinforcement Learning
97. ICML 2024Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problem
98. ICML 2024Finite Time Logarithmic Regret Bounds for Self-Tuning Regulation
99. ICML 2024Finite-Time Convergence and Sample Complexity of Actor-Critic Multi-Objective Reinforcement Learning
100. ICML 2024Foundation Policies with Hilbert Representations

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.