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

2,985 papers, page 53 of 60

2601. NeurIPS 2026LookThere! Sparse Vision by Reinforced Selection
2602. NeurIPS 2026Loop-Free Inverse Reinforcement Learning via Sequential Value Recovery
2603. NeurIPS 2026LoopRPT: Reinforcement Pre-Training for Looped Language Models
2604. NeurIPS 2026Low-Rank Adaptation for Critic Learning in Off-Policy Reinforcement Learning
2605. NeurIPS 2026LucidNFT: LR-Anchored Multi-Reward Preference Optimization for Flow-Based Real-World Super-Resolution
2606. NeurIPS 2026Lyapunov-Driven Optimistic Learning for Online Scheduling with Multi-Stage Tasks
2607. NeurIPS 2026Managing Self-Learning Experts under Per-Round Budget Constraints
2608. NeurIPS 2026Manifold-weighted neural networks
2609. NeurIPS 2026MaPP: A Unified Marginalized Posterior-Predictive Framework for Data-Efficient RLVR
2610. NeurIPS 2026MASTARS: Multi-Agent Sequential Trajectory Augmentation with Return-Conditioned Subgoals
2611. NeurIPS 2026MaxIM: Maximally Informative Incremental Summarization via Reinforcement Learning
2612. NeurIPS 2026Mean-Field Control on Sparse Graphs: From Local Limits to GNNs via Neighborhood Distributions
2613. NeurIPS 2026Measure Less, Know More: Self-Supervised Test-Time Feature Acquisition
2614. NeurIPS 2026Measuring and Strengthening Behavioral Suppression in Language Models
2615. NeurIPS 2026Measuring Coherence in Predictive Models
2616. NeurIPS 2026MedExAgent: Training LLM Agents to Ask, Examine, and Diagnose in Noisy Clinical Environments
2617. NeurIPS 2026Medical LLMs as Medical World Models: Unified Policy-Dynamics Learning with Test-Time Search
2618. NeurIPS 2026MedIGen: Reliable Medical Illustration Generation via Interleaved Introspective Reasoning
2619. NeurIPS 2026MedVIGOR: Visual Evidence Internalization for Observation-Driven Reasoning in Medical VLMs
2620. NeurIPS 2026Memory-R2: Fair Credit Assignment for Long-Horizon Memory-Augmented LLM Agents
2621. NeurIPS 2026Meta-Cognitive Memory Policy Optimization for Long-Horizon LLM Agents
2622. NeurIPS 2026Meta-Learning Preferences for Multilingual LLM Alignment
2623. NeurIPS 2026Meta-Reinforcement Learning with Zero-Shot Reinforcement Learning
2624. NeurIPS 2026Meta-TTRL: A Metacognitive Framework for Self-Improving Test-Time Reinforcement Learning for T2I Generation in Unified Multimodal Models
2625. NeurIPS 2026Mind the Gap: Information Disadvantage as a Learning Signal in Cooperative MARL
2626. NeurIPS 2026MIRA: Reinforcing Multimodal Reasoning via Deceptive Contextual Augmentation
2627. NeurIPS 2026Mitigating Compounding Errors in Online Reinforcement Learning via Optimal Transport Regularized Flow Matching
2628. NeurIPS 2026Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization
2629. NeurIPS 2026Mitigating Overgeneralization in RND via Spectral Target Design
2630. NeurIPS 2026Mitigating Retaliatory Algorithmic Collusion in Repeated Games
2631. NeurIPS 2026Mitigating Reward Hacking via Task Representations
2632. NeurIPS 2026Model-based Bootstrap of Controlled Markov Chains
2633. NeurIPS 2026Modeling quantum neural network gradient with reinforcement learning
2634. NeurIPS 2026Modelling Opinion Dynamics at Scale with Deep MARL
2635. NeurIPS 2026MOSAIC: Scaling Long-Horizon Language Agents via Multi-Scale Adaptive Inference Control
2636. NeurIPS 2026MPQ: A Message-Passing View of Post-Training Quantization
2637. NeurIPS 2026Mult-DPO: Multinomial Direct Preference Optimization for Recommender Systems
2638. NeurIPS 2026Multi-Objective Reinforcement Learning Using Routed Ensembles and Trajectory Attribution
2639. NeurIPS 2026Multi-Stage Planning from Single-Stage Data: Reinforcement Learning Helps Composition but Requires Anchoring
2640. NeurIPS 2026Multi-Step Likelihood-Ratio Correction for Reinforcement Learning with Verifiable Rewards
2641. NeurIPS 2026Multi-Turn RL Makes Small Language Model Competitive for Optimization Modeling
2642. NeurIPS 2026NASDAQ: Normalized Observation Space Dynamics-Augmented Q-Learning
2643. NeurIPS 2026Nearly Optimal Fixed-Confidence Best-Arm Identification with 1-Bit Feedback
2644. NeurIPS 2026Need-Aware Multi-Objective Reinforcement Learning for Emotionally Intelligent LLM Agents
2645. NeurIPS 2026Neglected Free Lunch from Post-training: Progress Advantage for LLM Agents
2646. NeurIPS 2026Nemotron-Cascade: Scaling Cascaded Reinforcement Learning for General-Purpose Reasoning Models
2647. NeurIPS 2026NestRL: A Nested Training Regime for Mutual Adaptation in Human–AI Teaming
2648. NeurIPS 2026Neural Chameleons: Language Models Can Learn to Hide Their Thoughts from Unseen Activation Monitors
2649. NeurIPS 2026Neural Garbage Collection: Learning to Forget while Learning to Reason
2650. NeurIPS 2026Neural Modal Decomposition: Architectural Priors from Observables

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.