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

466 papers, page 2 of 10

51. NeurIPS 2024BECAUSE: Bilinear Causal Representation for Generalizable Offline Model-based Reinforcement Learning
52. NeurIPS 2024Belief-State Query Policies for User-Aligned POMDPs
53. NeurIPS 2024BenchMARL: Benchmarking Multi-Agent Reinforcement Learning
54. NeurIPS 2024Beyond Optimism: Exploration With Partially Observable Rewards
55. NeurIPS 2024Beyond Single Stationary Policies: Meta-Task Players as Naturally Superior Collaborators
56. NeurIPS 2024Bias and Volatility: A Statistical Framework for Evaluating Large Language Model's Stereotypes and the Associated Generation Inconsistency
57. NeurIPS 2024Bigger, Regularized, Optimistic: scaling for compute and sample efficient continuous control
58. NeurIPS 2024Bisimulation Metrics are Optimal Transport Distances, and Can be Computed Efficiently
59. NeurIPS 2024Boosting Sample Efficiency and Generalization in Multi-agent Reinforcement Learning via Equivariance
60. NeurIPS 2024Boosting Text-to-Video Generative Model with MLLMs Feedback
61. NeurIPS 2024BricksRL: A Platform for Democratizing Robotics and Reinforcement Learning Research and Education with LEGO
62. NeurIPS 2024Bridging Model-Based Optimization and Generative Modeling via Conservative Fine-Tuning of Diffusion Models
63. NeurIPS 2024C-GAIL: Stabilizing Generative Adversarial Imitation Learning with Control Theory
64. NeurIPS 2024CALE: Continuous Arcade Learning Environment
65. NeurIPS 2024Can large language models explore in-context?
66. NeurIPS 2024Can Learned Optimization Make Reinforcement Learning Less Difficult?
67. NeurIPS 2024Carrot and Stick: Eliciting Comparison Data and Beyond
68. NeurIPS 2024Catastrophic Goodhart: regularizing RLHF with KL divergence does not mitigate heavy-tailed reward misspecification
69. NeurIPS 2024CE-NAS: An End-to-End Carbon-Efficient Neural Architecture Search Framework
70. NeurIPS 2024CleanDiffuser: An Easy-to-use Modularized Library for Diffusion Models in Decision Making
71. NeurIPS 2024Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement Learning
72. NeurIPS 2024Compositional Automata Embeddings for Goal-Conditioned Reinforcement Learning
73. NeurIPS 2024Confident Natural Policy Gradient for Local Planning in $q_\pi$-realizable Constrained MDPs
74. NeurIPS 2024Constrained Latent Action Policies for Model-Based Offline Reinforcement Learning
75. NeurIPS 2024Contextual Bilevel Reinforcement Learning for Incentive Alignment
76. NeurIPS 2024Controlled maximal variability along with reliable performance in recurrent neural networks
77. NeurIPS 2024CooHOI: Learning Cooperative Human-Object Interaction with Manipulated Object Dynamics
78. NeurIPS 2024Corruption-Robust Linear Bandits: Minimax Optimality and Gap-Dependent Misspecification
79. NeurIPS 2024DACO: Towards Application-Driven and Comprehensive Data Analysis via Code Generation
80. NeurIPS 2024Decision Mamba: A Multi-Grained State Space Model with Self-Evolution Regularization for Offline RL
81. NeurIPS 2024Decision Mamba: Reinforcement Learning via Hybrid Selective Sequence Modeling
82. NeurIPS 2024Decomposed Prompt Decision Transformer for Efficient Unseen Task Generalization
83. NeurIPS 2024Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers
84. NeurIPS 2024Designs for Enabling Collaboration in Human-Machine Teaming via Interactive and Explainable Systems
85. NeurIPS 2024Deterministic Policies for Constrained Reinforcement Learning in Polynomial Time
86. NeurIPS 2024Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning
87. NeurIPS 2024Differentiable Quantum Computing for Large-scale Linear Control
88. NeurIPS 2024Differentially Private Reinforcement Learning with Self-Play
89. NeurIPS 2024DiffLight: A Partial Rewards Conditioned Diffusion Model for Traffic Signal Control with Missing Data
90. NeurIPS 2024DiffPhyCon: A Generative Approach to Control Complex Physical Systems
91. NeurIPS 2024DiffTORI: Differentiable Trajectory Optimization for Deep Reinforcement and Imitation Learning
92. NeurIPS 2024DiffuserLite: Towards Real-time Diffusion Planning
93. NeurIPS 2024Diffusion Actor-Critic with Entropy Regulator
94. NeurIPS 2024Diffusion for World Modeling: Visual Details Matter in Atari
95. NeurIPS 2024Diffusion Imitation from Observation
96. NeurIPS 2024Diffusion Policies Creating a Trust Region for Offline Reinforcement Learning
97. NeurIPS 2024Diffusion Spectral Representation for Reinforcement Learning
98. NeurIPS 2024Diffusion-based Curriculum Reinforcement Learning
99. NeurIPS 2024Diffusion-based Reinforcement Learning via Q-weighted Variational Policy Optimization
100. NeurIPS 2024Diffusion-DICE: In-Sample Diffusion Guidance for Offline Reinforcement Learning

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.