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 134 of 138

6651. NeurIPS 2023Likelihood Ratio Confidence Sets for Sequential Decision Making
6652. NeurIPS 2023LIMA: Less Is More for Alignment
6653. NeurIPS 2023List and Certificate Complexities in Replicable Learning
6654. NeurIPS 2023LogSpecT: Feasible Graph Learning Model from Stationary Signals with Recovery Guarantees
6655. NeurIPS 2023Long-Term Fairness with Unknown Dynamics
6656. NeurIPS 2023Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline RL
6657. NeurIPS 2023Loss Dynamics of Temporal Difference Reinforcement Learning
6658. NeurIPS 2023Macro Placement by Wire-Mask-Guided Black-Box Optimization
6659. NeurIPS 2023MAG-GNN: Reinforcement Learning Boosted Graph Neural Network
6660. NeurIPS 2023Making Scalable Meta Learning Practical
6661. NeurIPS 2023Managing Temporal Resolution in Continuous Value Estimation: A Fundamental Trade-off
6662. NeurIPS 2023Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration
6663. NeurIPS 2023Maximum State Entropy Exploration using Predecessor and Successor Representations
6664. NeurIPS 2023MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning
6665. NeurIPS 2023MIMEx: Intrinsic Rewards from Masked Input Modeling
6666. NeurIPS 2023Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks
6667. NeurIPS 2023Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms
6668. NeurIPS 2023Model-Free Active Exploration in Reinforcement Learning
6669. NeurIPS 2023Model-free Posterior Sampling via Learning Rate Randomization
6670. NeurIPS 2023Model-Free Reinforcement Learning with the Decision-Estimation Coefficient
6671. NeurIPS 2023Monte Carlo Tree Search with Boltzmann Exploration
6672. NeurIPS 2023MoVie: Visual Model-Based Policy Adaptation for View Generalization
6673. NeurIPS 2023Multi-Agent First Order Constrained Optimization in Policy Space
6674. NeurIPS 2023Multi-Agent Meta-Reinforcement Learning: Sharper Convergence Rates with Task Similarity
6675. NeurIPS 2023Multi-Modal Inverse Constrained Reinforcement Learning from a Mixture of Demonstrations
6676. NeurIPS 2023Multi-Objective Intrinsic Reward Learning for Conversational Recommender Systems
6677. NeurIPS 2023Multi-Step Generalized Policy Improvement by Leveraging Approximate Models
6678. NeurIPS 2023Mutual Information Regularized Offline Reinforcement Learning
6679. NeurIPS 2023Mutual-Information Regularized Multi-Agent Policy Iteration
6680. NeurIPS 2023Natural Actor-Critic for Robust Reinforcement Learning with Function Approximation
6681. NeurIPS 2023Natural Language Instruction-following with Task-related Language Development and Translation
6682. NeurIPS 2023Nearly Tight Bounds For Differentially Private Multiway Cut
6683. NeurIPS 2023Necessary and Sufficient Conditions for Optimal Decision Trees using Dynamic Programming
6684. NeurIPS 2023NetHack is Hard to Hack
6685. NeurIPS 2023Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning
6686. NeurIPS 2023Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement
6687. NeurIPS 2023No-Regret Online Reinforcement Learning with Adversarial Losses and Transitions
6688. NeurIPS 2023Noether Embedding: Efficient Learning of Temporal Regularities
6689. NeurIPS 2023Non-Convex Bilevel Optimization with Time-Varying Objective Functions
6690. NeurIPS 2023NVFi: Neural Velocity Fields for 3D Physics Learning from Dynamic Videos
6691. NeurIPS 2023ODE-based Recurrent Model-free Reinforcement Learning for POMDPs
6692. NeurIPS 2023OFCOURSE: A Multi-Agent Reinforcement Learning Environment for Order Fulfillment
6693. NeurIPS 2023Off-Policy Evaluation for Human Feedback
6694. NeurIPS 2023Offline Imitation Learning with Variational Counterfactual Reasoning
6695. NeurIPS 2023Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage
6696. NeurIPS 2023Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value Regularization
6697. NeurIPS 2023Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management
6698. NeurIPS 2023Offline Reinforcement Learning with Differential Privacy
6699. NeurIPS 2023Offline RL with Discrete Proxy Representations for Generalizability in POMDPs
6700. NeurIPS 2023On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes

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.