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

1,190 papers, page 19 of 24

901. NeurIPS 2024Grounded Answers for Multi-agent Decision-making Problem through Generative World Model
902. NeurIPS 2024Grounding Multimodal Large Language Models in Actions
903. NeurIPS 2024Group Robust Preference Optimization in Reward-free RLHF
904. NeurIPS 2024GTA: Generative Trajectory Augmentation with Guidance for Offline Reinforcement Learning
905. NeurIPS 2024GUIDE: Real-Time Human-Shaped Agents
906. NeurIPS 2024Hierarchical Programmatic Option Framework
907. NeurIPS 2024How does Inverse RL Scale to Large State Spaces? A Provably Efficient Approach
908. NeurIPS 2024How to Solve Contextual Goal-Oriented Problems with Offline Datasets?
909. NeurIPS 2024HumanVLA: Towards Vision-Language Directed Object Rearrangement by Physical Humanoid
910. NeurIPS 2024Hybrid Reinforcement Learning Breaks Sample Size Barriers In Linear MDPs
911. NeurIPS 2024Identifying Latent State-Transition Processes for Individualized Reinforcement Learning
912. NeurIPS 2024Identifying Selections for Unsupervised Subtask Discovery
913. NeurIPS 2024Imitating Language via Scalable Inverse Reinforcement Learning
914. NeurIPS 2024Implicit Curriculum in Procgen Made Explicit
915. NeurIPS 2024Improving Deep Reinforcement Learning by Reducing the Chain Effect of Value and Policy Churn
916. NeurIPS 2024Improving Environment Novelty Quantification for Effective Unsupervised Environment Design
917. NeurIPS 2024In-Trajectory Inverse Reinforcement Learning: Learn Incrementally Before an Ongoing Trajectory Terminates
918. NeurIPS 2024Incremental Learning of Retrievable Skills For Efficient Continual Task Adaptation
919. NeurIPS 2024InfoRM: Mitigating Reward Hacking in RLHF via Information-Theoretic Reward Modeling
920. NeurIPS 2024Integrating Suboptimal Human Knowledge with Hierarchical Reinforcement Learning for Large-Scale Multiagent Systems
921. NeurIPS 2024Interpretable Concept Bottlenecks to Align Reinforcement Learning Agents
922. NeurIPS 2024Interpreting Learned Feedback Patterns in Large Language Models
923. NeurIPS 2024Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning
924. NeurIPS 2024Is Mamba Compatible with Trajectory Optimization in Offline Reinforcement Learning?
925. NeurIPS 2024Is O(log N) practical? Near-Equivalence Between Delay Robustness and Bounded Regret in Bandits and RL
926. NeurIPS 2024Is Value Learning Really the Main Bottleneck in Offline RL?
927. NeurIPS 2024Iteratively Refined Behavior Regularization for Offline Reinforcement Learning
928. NeurIPS 2024iVideoGPT: Interactive VideoGPTs are Scalable World Models
929. NeurIPS 2024JaxMARL: Multi-Agent RL Environments and Algorithms in JAX
930. NeurIPS 2024Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement Learning
931. NeurIPS 2024KALM: Knowledgeable Agents by Offline Reinforcement Learning from Large Language Model Rollouts
932. NeurIPS 2024Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret Algorithm
933. NeurIPS 2024Language Grounded Multi-agent Reinforcement Learning with Human-interpretable Communication
934. NeurIPS 2024Large Language Models Play StarCraft II:Benchmarks and A Chain of Summarization Approach
935. NeurIPS 2024Last-Iterate Global Convergence of Policy Gradients for Constrained Reinforcement Learning
936. NeurIPS 2024Latent Learning Progress Drives Autonomous Goal Selection in Human Reinforcement Learning
937. NeurIPS 2024Latent Plan Transformer for Trajectory Abstraction: Planning as Latent Space Inference
938. NeurIPS 2024Learning an Actionable Discrete Diffusion Policy via Large-Scale Actionless Video Pre-Training
939. NeurIPS 2024Learning Distinguishable Trajectory Representation with Contrastive Loss
940. NeurIPS 2024Learning Equilibria in Adversarial Team Markov Games: A Nonconvex-Hidden-Concave Min-Max Optimization Problem
941. NeurIPS 2024Learning Formal Mathematics From Intrinsic Motivation
942. NeurIPS 2024Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate
943. NeurIPS 2024Learning General Parameterized Policies for Infinite Horizon Average Reward Constrained MDPs via Primal-Dual Policy Gradient Algorithm
944. NeurIPS 2024Learning Goal-Conditioned Representations for Language Reward Models
945. NeurIPS 2024Learning in Markov Games with Adaptive Adversaries: Policy Regret, Fundamental Barriers, and Efficient Algorithms
946. NeurIPS 2024Learning Multimodal Behaviors from Scratch with Diffusion Policy Gradient
947. NeurIPS 2024Learning Successor Features the Simple Way
948. NeurIPS 2024Learning to Assist Humans without Inferring Rewards
949. NeurIPS 2024Learning to Balance Altruism and Self-interest Based on Empathy in Mixed-Motive Games
950. NeurIPS 2024Learning to Cooperate with Humans using Generative Agents

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.