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

760 papers, page 5 of 16

201. NeurIPS 2026Edit-R2: Context-Aware Reinforcement Learning for Multi-Turn Image Editing
202. NeurIPS 2026Effectiveness of Curriculum Learning Depends on Reward Sparsity and Competing Optima
203. NeurIPS 2026Efficient Diffusion Policy Fine Tuning with Latent Noise Representation Bridging
204. NeurIPS 2026Efficient Gradient-Aware Asynchronous Reinforcement Learning for LLM Post-Training
205. NeurIPS 2026Efficient Off-Policy RL for Video Generation via Forward-Consistent Reward Matching
206. NeurIPS 2026Efficient Test-Time Adaptation For Robot Policies
207. NeurIPS 2026EfficientRollout: System-Aware Self-Speculative Decoding for RL Rollouts
208. NeurIPS 2026Enhancing Speech Large Language Models through Reinforced Behavior Alignment
209. NeurIPS 2026Entropy Dynamics of Agent Reinforcement Learning
210. NeurIPS 2026Equivariant Reinforcement Learning for Clifford Quantum Circuit Synthesis
211. NeurIPS 2026Error-Guided Bellman Calibration for Semi-Offline Value Estimation
212. NeurIPS 2026Escaping Path Mirages in Offline Goal-Conditioned Reinforcement Learning
213. NeurIPS 2026ESSAM: A Novel Competitive Evolution Strategies Approach to Reinforcement Learning for Memory Efficient LLMs Fine-Tuning
214. NeurIPS 2026EventLens: Event-Structure Reinforcement Learning for Video Understanding
215. NeurIPS 2026EvoGround: Self-Evolving Video Agents for Video Temporal Grounding
216. NeurIPS 2026Evolutionary System Prompt Learning for Reinforcement Learning in LLMs
217. NeurIPS 2026EvoMM: Reinforced Self-Evolving Multimodal Agentic Memory
218. NeurIPS 2026EvoOptiGraph: Weakness-Driven Coevolution via Graph-Based Structural Generation for Optimization Modeling
219. NeurIPS 2026Expanding LLM Agent Boundaries with Strategy-Guided Exploration
220. NeurIPS 2026Explaining and Preventing Alignment Collapse in Iterative RLHF
221. NeurIPS 2026ExpLang: Improved Exploration and Exploitation in LLM Reasoning with On-Policy Thinking Language Selection
222. NeurIPS 2026Exploration as Constrained Policy-Space Optimization
223. NeurIPS 2026Exploration via Exploitation: The Blessing of Reward Diversity in Personalized Federated RL
224. NeurIPS 2026Exploring Lifelong Adaptation: In-Context Reinforcement Learning in Non-Stationary Environments
225. NeurIPS 2026Exploring Starts Are Not Enough: Counterexamples and a Fix for Monte Carlo Exploring Starts
226. NeurIPS 2026Extracting Training Data from Diffusion Language Models via Infilling
227. NeurIPS 2026Fair Range k-Supplier Clustering in Offline and Streaming Models
228. NeurIPS 2026Finetuning with Sampling: Make SFT Generalize, Not Forget
229. NeurIPS 2026Finite Time Analysis of Risk-Sensitive RL via Noisy Power Iteration
230. NeurIPS 2026Finite-Resolution Decision Sufficiency for Linear Optimization
231. NeurIPS 2026Finite-Sample Convergence in Networked Average Reward MARL: Decentralization Pitfalls and Entropy Remedies
232. NeurIPS 2026FiRe: Fine-grained Multimodal Reasoning for Enhanced Image Generation
233. NeurIPS 2026Fisher-Glass: Tail Sample Complexity from Nuisance-Projected Fisher Information
234. NeurIPS 2026Fisher-R1: Training LLM Agents for Reliable Hypothesis Testing
235. NeurIPS 2026FLAG: Flow Policy MaxEnt-RL by Latent Augmented Guidance
236. NeurIPS 2026Flow Matching for Offline Reinforcement Learning with Discrete Actions
237. NeurIPS 2026Flow Matching Policy Optimization with Mirror Descent and Entropy Constraints
238. NeurIPS 2026Flow Matching Reinforcement Learning via SDE Inference
239. NeurIPS 2026Flow-DPPO: Divergence Proximal Policy Optimization for Flow Matching Models
240. NeurIPS 2026FlowSteer: Towards Agents Designing Agentic Workflows via Reinforced Progressive Canvas Editing
241. NeurIPS 2026Focal Reward: Balanced Reinforcement Learning under Rubric-Based Rewards
242. NeurIPS 2026Focusing Influence Mechanism for Multi-Agent Reinforcement Learning
243. NeurIPS 2026Forgetting to Improve: Principled Data Removal in Active Learning
244. NeurIPS 2026Forward Shapley Scoring for Non-Myopic Active Feature Acquisition
245. NeurIPS 2026FoundSurface: Feedforward Ray-Based 3D Scene Surface Reconstruction from Unposed Images
246. NeurIPS 2026FracEncoder: Towards Adaptive Cognitive Trajectories via Fractional-Order Context Encoding
247. NeurIPS 2026From Anti-Forgetting to Fast Adaptation: Continual Reinforcement Learning with World Models
248. NeurIPS 2026From Approximation to Computation: Universal Power of Deep Narrow Networks at Constant Width
249. NeurIPS 2026From Expert Knowledge to Optimization Modeling: Prototype-Based Data Synthesis and Logical Reinforcement Learning
250. NeurIPS 2026From Ideas to Code: Tree-structured Policy Optimization for Automated Algorithm Design with LLMs

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.