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 133 of 138

6601. NeurIPS 2023Importance-aware Co-teaching for Offline Model-based Optimization
6602. NeurIPS 2023Improved Bayesian Regret Bounds for Thompson Sampling in Reinforcement Learning
6603. NeurIPS 2023Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates
6604. NeurIPS 2023Inferring the Future by Imagining the Past
6605. NeurIPS 2023Information Design in Multi-Agent Reinforcement Learning
6606. NeurIPS 2023Information Maximizing Curriculum: A Curriculum-Based Approach for Learning Versatile Skills
6607. NeurIPS 2023Instructing Goal-Conditioned Reinforcement Learning Agents with Temporal Logic Objectives
6608. NeurIPS 2023Interaction Measures, Partition Lattices and Kernel Tests for High-Order Interactions
6609. NeurIPS 2023InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback
6610. NeurIPS 2023Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstraction
6611. NeurIPS 2023Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach
6612. NeurIPS 2023Inverse Preference Learning: Preference-based RL without a Reward Function
6613. NeurIPS 2023Inverse Reinforcement Learning with the Average Reward Criterion
6614. NeurIPS 2023Is RLHF More Difficult than Standard RL? A Theoretical Perspective
6615. NeurIPS 2023Iterative Reachability Estimation for Safe Reinforcement Learning
6616. NeurIPS 2023Iteratively Learn Diverse Strategies with State Distance Information
6617. NeurIPS 2023Katakomba: Tools and Benchmarks for Data-Driven NetHack
6618. NeurIPS 2023Keep Various Trajectories: Promoting Exploration of Ensemble Policies in Continuous Control
6619. NeurIPS 2023Kernelized Reinforcement Learning with Order Optimal Regret Bounds
6620. NeurIPS 2023Koopman Kernel Regression
6621. NeurIPS 2023KuaiSim: A Comprehensive Simulator for Recommender Systems
6622. NeurIPS 2023Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information
6623. NeurIPS 2023Language Model Alignment with Elastic Reset
6624. NeurIPS 2023Language Models can Solve Computer Tasks
6625. NeurIPS 2023Large Language Models Are Semi-Parametric Reinforcement Learning Agents
6626. NeurIPS 2023Large Language Models can Implement Policy Iteration
6627. NeurIPS 2023Latent exploration for Reinforcement Learning
6628. NeurIPS 2023Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback
6629. NeurIPS 2023Learning Better with Less: Effective Augmentation for Sample-Efficient Visual Reinforcement Learning
6630. NeurIPS 2023Learning Dynamic Attribute-factored World Models for Efficient Multi-object Reinforcement Learning
6631. NeurIPS 2023Learning Exponential Families from Truncated Samples
6632. NeurIPS 2023Learning from Active Human Involvement through Proxy Value Propagation
6633. NeurIPS 2023Learning from Visual Observation via Offline Pretrained State-to-Go Transformer
6634. NeurIPS 2023Learning Generalizable Agents via Saliency-guided Features Decorrelation
6635. NeurIPS 2023Learning Multi-agent Behaviors from Distributed and Streaming Demonstrations
6636. NeurIPS 2023Learning non-Markovian Decision-Making from State-only Sequences
6637. NeurIPS 2023Learning Regularized Monotone Graphon Mean-Field Games
6638. NeurIPS 2023Learning Score-based Grasping Primitive for Human-assisting Dexterous Grasping
6639. NeurIPS 2023Learning Shared Safety Constraints from Multi-task Demonstrations
6640. NeurIPS 2023Learning Space-Time Continuous Latent Neural PDEs from Partially Observed States
6641. NeurIPS 2023Learning the Efficient Frontier
6642. NeurIPS 2023Learning Time-Invariant Representations for Individual Neurons from Population Dynamics
6643. NeurIPS 2023Learning to Discover Skills through Guidance
6644. NeurIPS 2023Learning to Influence Human Behavior with Offline Reinforcement Learning
6645. NeurIPS 2023Learning to Modulate pre-trained Models in RL
6646. NeurIPS 2023Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Opt
6647. NeurIPS 2023Learning Unseen Modality Interaction
6648. NeurIPS 2023Learning World Models with Identifiable Factorization
6649. NeurIPS 2023Lending Interaction Wings to Recommender Systems with Conversational Agents
6650. NeurIPS 2023LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios

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.