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

2,494 papers, page 46 of 50

2251. NeurIPS 2023Learning Time-Invariant Representations for Individual Neurons from Population Dynamics
2252. NeurIPS 2023Learning to Discover Skills through Guidance
2253. NeurIPS 2023Learning to Influence Human Behavior with Offline Reinforcement Learning
2254. NeurIPS 2023Learning to Modulate pre-trained Models in RL
2255. NeurIPS 2023Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Opt
2256. NeurIPS 2023Learning Unseen Modality Interaction
2257. NeurIPS 2023Learning World Models with Identifiable Factorization
2258. NeurIPS 2023Lending Interaction Wings to Recommender Systems with Conversational Agents
2259. NeurIPS 2023LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios
2260. NeurIPS 2023Likelihood Ratio Confidence Sets for Sequential Decision Making
2261. NeurIPS 2023LIMA: Less Is More for Alignment
2262. NeurIPS 2023List and Certificate Complexities in Replicable Learning
2263. NeurIPS 2023LogSpecT: Feasible Graph Learning Model from Stationary Signals with Recovery Guarantees
2264. NeurIPS 2023Long-Term Fairness with Unknown Dynamics
2265. NeurIPS 2023Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline RL
2266. NeurIPS 2023Loss Dynamics of Temporal Difference Reinforcement Learning
2267. NeurIPS 2023Macro Placement by Wire-Mask-Guided Black-Box Optimization
2268. NeurIPS 2023MAG-GNN: Reinforcement Learning Boosted Graph Neural Network
2269. NeurIPS 2023Making Scalable Meta Learning Practical
2270. NeurIPS 2023Managing Temporal Resolution in Continuous Value Estimation: A Fundamental Trade-off
2271. NeurIPS 2023Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration
2272. NeurIPS 2023Maximum State Entropy Exploration using Predecessor and Successor Representations
2273. NeurIPS 2023MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning
2274. NeurIPS 2023MIMEx: Intrinsic Rewards from Masked Input Modeling
2275. NeurIPS 2023Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks
2276. NeurIPS 2023Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms
2277. NeurIPS 2023Model-Free Active Exploration in Reinforcement Learning
2278. NeurIPS 2023Model-free Posterior Sampling via Learning Rate Randomization
2279. NeurIPS 2023Model-Free Reinforcement Learning with the Decision-Estimation Coefficient
2280. NeurIPS 2023Monte Carlo Tree Search with Boltzmann Exploration
2281. NeurIPS 2023MoVie: Visual Model-Based Policy Adaptation for View Generalization
2282. NeurIPS 2023Multi-Agent First Order Constrained Optimization in Policy Space
2283. NeurIPS 2023Multi-Agent Meta-Reinforcement Learning: Sharper Convergence Rates with Task Similarity
2284. NeurIPS 2023Multi-Modal Inverse Constrained Reinforcement Learning from a Mixture of Demonstrations
2285. NeurIPS 2023Multi-Objective Intrinsic Reward Learning for Conversational Recommender Systems
2286. NeurIPS 2023Multi-Step Generalized Policy Improvement by Leveraging Approximate Models
2287. NeurIPS 2023Mutual Information Regularized Offline Reinforcement Learning
2288. NeurIPS 2023Mutual-Information Regularized Multi-Agent Policy Iteration
2289. NeurIPS 2023Natural Actor-Critic for Robust Reinforcement Learning with Function Approximation
2290. NeurIPS 2023Natural Language Instruction-following with Task-related Language Development and Translation
2291. NeurIPS 2023Nearly Tight Bounds For Differentially Private Multiway Cut
2292. NeurIPS 2023Necessary and Sufficient Conditions for Optimal Decision Trees using Dynamic Programming
2293. NeurIPS 2023NetHack is Hard to Hack
2294. NeurIPS 2023Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning
2295. NeurIPS 2023Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement
2296. NeurIPS 2023No-Regret Online Reinforcement Learning with Adversarial Losses and Transitions
2297. NeurIPS 2023Noether Embedding: Efficient Learning of Temporal Regularities
2298. NeurIPS 2023Non-Convex Bilevel Optimization with Time-Varying Objective Functions
2299. NeurIPS 2023NVFi: Neural Velocity Fields for 3D Physics Learning from Dynamic Videos
2300. NeurIPS 2023ODE-based Recurrent Model-free Reinforcement Learning for POMDPs

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.