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

6,885 papers, page 128 of 138

6351. ICML 2023Semi-Offline Reinforcement Learning for Optimized Text Generation
6352. ICML 2023Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories
6353. ICML 2023Semiparametrically Efficient Off-Policy Evaluation in Linear Markov Decision Processes
6354. ICML 2023Set-membership Belief State-based Reinforcement Learning for POMDPs
6355. ICML 2023Sharp Variance-Dependent Bounds in Reinforcement Learning: Best of Both Worlds in Stochastic and Deterministic Environments
6356. ICML 2023Shortest Edit Path Crossover: A Theory-driven Solution to the Permutation Problem in Evolutionary Neural Architecture Search
6357. ICML 2023Simple Embodied Language Learning as a Byproduct of Meta-Reinforcement Learning
6358. ICML 2023Simplified Temporal Consistency Reinforcement Learning
6359. ICML 2023SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement Learning
6360. ICML 2023Social learning spontaneously emerges by searching optimal heuristics with deep reinforcement learning
6361. ICML 2023STEERING : Stein Information Directed Exploration for Model-Based Reinforcement Learning
6362. ICML 2023Stein Variational Goal Generation for adaptive Exploration in Multi-Goal Reinforcement Learning
6363. ICML 2023STEP: Learning N:M Structured Sparsity Masks from Scratch with Precondition
6364. ICML 2023StriderNet: A Graph Reinforcement Learning Approach to Optimize Atomic Structures on Rough Energy Landscapes
6365. ICML 2023Subequivariant Graph Reinforcement Learning in 3D Environments
6366. ICML 2023Supported Trust Region Optimization for Offline Reinforcement Learning
6367. ICML 2023Target-based Surrogates for Stochastic Optimization
6368. ICML 2023TGRL: An Algorithm for Teacher Guided Reinforcement Learning
6369. ICML 2023The Benefits of Model-Based Generalization in Reinforcement Learning
6370. ICML 2023The Dormant Neuron Phenomenon in Deep Reinforcement Learning
6371. ICML 2023The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation
6372. ICML 2023The Regret of Exploration and the Control of Bad Episodes in Reinforcement Learning
6373. ICML 2023The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation
6374. ICML 2023The Unintended Consequences of Discount Regularization: Improving Regularization in Certainty Equivalence Reinforcement Learning
6375. ICML 2023The Virtues of Laziness in Model-based RL: A Unified Objective and Algorithms
6376. ICML 2023The Wisdom of Hindsight Makes Language Models Better Instruction Followers
6377. ICML 2023Tighter Information-Theoretic Generalization Bounds from Supersamples
6378. ICML 2023Toward Efficient Gradient-Based Value Estimation
6379. ICML 2023Towards a better understanding of representation dynamics under TD-learning
6380. ICML 2023Towards Learning to Imitate from a Single Video Demonstration
6381. ICML 2023Towards Robust and Safe Reinforcement Learning with Benign Off-policy Data
6382. ICML 2023Towards Theoretical Understanding of Inverse Reinforcement Learning
6383. ICML 2023Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning
6384. ICML 2023Truncating Trajectories in Monte Carlo Reinforcement Learning
6385. ICML 2023Tuning Computer Vision Models With Task Rewards
6386. ICML 2023Two Losses Are Better Than One: Faster Optimization Using a Cheaper Proxy
6387. ICML 2023Understanding and Generalizing Contrastive Learning from the Inverse Optimal Transport Perspective
6388. ICML 2023Understanding Plasticity in Neural Networks
6389. ICML 2023Understanding Self-Predictive Learning for Reinforcement Learning
6390. ICML 2023Understanding the Complexity Gains of Single-Task RL with a Curriculum
6391. ICML 2023Universal Morphology Control via Contextual Modulation
6392. ICML 2023VA-learning as a more efficient alternative to Q-learning
6393. ICML 2023Variance Control for Distributional Reinforcement Learning
6394. ICML 2023Variational Curriculum Reinforcement Learning for Unsupervised Discovery of Skills
6395. ICML 2023VectorMapNet: End-to-end Vectorized HD Map Learning
6396. ICML 2023Warm-Start Actor-Critic: From Approximation Error to Sub-optimality Gap
6397. ICML 2023Weighted Sampling without Replacement for Deep Top-$k$ Classification
6398. ICML 2023What can online reinforcement learning with function approximation benefit from general coverage conditions?
6399. ICML 2023What is Essential for Unseen Goal Generalization of Offline Goal-conditioned RL?
6400. ICML 2023When is Realizability Sufficient for Off-Policy 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.