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 36 of 138

1751. ICML 2026ML-Agent: Reinforcing LLM Agents for Autonomous Machine Learning Engineering
1752. ICML 2026MM-DeepResearch: A Simple and Effective Multimodal Agentic Search Baseline
1753. ICML 2026MMKU-Bench: A Multimodal Update Benchmark for Diverse Visual Knowledge
1754. ICML 2026Model-Free Robust Average-Reward Reinforcement Learning with Sample Complexity Analysis
1755. ICML 2026Models Under SCOPE: Scalable and Controllable Routing via Pre-hoc Reasoning
1756. ICML 2026Moment Matching Q-Learning
1757. ICML 2026Monitorability as a Free Gift: How RLVR Spontaneously Aligns Reasoning
1758. ICML 2026MotionGRPO: Overcoming Low Intra-Group Diversity in GRPO-Based Egocentric Motion Recovery
1759. ICML 2026MRPO: Magnitude-Regularized Policy Optimization via L1 Constraints
1760. ICML 2026Multi-Agent Reinforcement Learning with Submodular Reward
1761. ICML 2026Multi-Task GRPO: Reliable LLM Reasoning Across Tasks
1762. ICML 2026Multi-timescale Reinforcement Learning by Value Reconstruction
1763. ICML 2026Multi-view Consistent Latent Action Learning for World Modeling and Control
1764. ICML 2026Multilingual Safety Alignment Via Sparse Weight Editing
1765. ICML 2026Multimodal Meta-Verifier with Explicit Structured Recalibration
1766. ICML 2026Multivariate Distributional Reinforcement Learning Using Sliced Divergences
1767. ICML 2026MVR-cache: Optimizing Semantic Caching via Multi-Vector Retrieval and Learned Prompt Segmentation
1768. ICML 2026Native Active Perception as Reasoning for Omni-Modal Understanding
1769. ICML 2026Native Parallel Reasoner: Reasoning in Parallelism via Self-Distilled Reinforcement Learning
1770. ICML 2026NavOL: Navigation Policy with Online Imitation Learning
1771. ICML 2026Near-Minimax Multi-Objective RL under Predictable Adversarial Preferences and Preference-Free Exploration in Linear MDPs
1772. ICML 2026Near-Optimal Regret for KL-Regularized Multi-Armed Bandits
1773. ICML 2026Near-Optimal Regret for Policy Optimization in Contextual MDPs with General Offline Function Approximation
1774. ICML 2026Neural–Evolutionary Symbolic Regression with Global Constraints: Constraint-Aware Decoding and Reward Shaping
1775. ICML 2026Neuro-evolutionary Continual Reinforcement Learning
1776. ICML 2026Noise as a Natural Regularizer in Markov Decision Processes: Connecting Environmental Stochasticity and Policy Simplicity
1777. ICML 2026Noise-corrected GRPO: From Noisy Rewards to Unbiased Gradients
1778. ICML 2026Noise-Guided Transport: Imitation Learning from Random Priors
1779. ICML 2026Noisy-Space Policy Gradient for Diffusion Policies in Offline Reinforcement Learning
1780. ICML 2026Non-Adversarial Imitation Learning Provably Free of Compounding Errors: The Value Flow Mechanism
1781. ICML 2026Non-Parametric Optimization for Scalable Learning in Stochastic Decision Problems
1782. ICML 2026Non-Uniform Noise-to-Signal Ratio in the REINFORCE Policy-Gradient Estimator
1783. ICML 2026NonZero: Interaction-Guided Exploration for Multi-Agent Monte Carlo Tree Search
1784. ICML 2026Off-Policy Evaluation for Missingness-Aware Policies in MDPs with Rewards Missing Not at Random
1785. ICML 2026Off-Policy Learning in Large Action Spaces: Optimization Matters More Than Estimation
1786. ICML 2026Offline Multi-Agent Reinforcement Learning via Sequential Score Decomposition
1787. ICML 2026Offline Reinforcement Learning of High-Quality Behaviors Under Robust Style Alignment
1788. ICML 2026Offline Reinforcement Learning with Generative Trajectory Policies
1789. ICML 2026Offline Reinforcement Learning with Universal Horizon Models
1790. ICML 2026OGPO: Sample Efficient Full-Finetuning of Generative Control Policies
1791. ICML 2026Omni-Perception Policy Optimization for Multimodal Emotion Reasoning
1792. ICML 2026OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization
1793. ICML 2026On Effectiveness and Efficiency of Agentic Tool-calling and RL Training
1794. ICML 2026On Group Relative Policy Optimization Collapse in Agent Search: The Lazy Likelihood-Displacement
1795. ICML 2026On Information Self-Locking in Reinforcement Learning for Active Reasoning of LLM agents
1796. ICML 2026On Robustness and Chain-of-Thought Consistency of RL-Finetuned VLMs
1797. ICML 2026On the Emergence of Implicit Curriculum in RLVR Learning Dynamics
1798. ICML 2026On the Entropy Dynamics in Reinforcement Fine-Tuning of Large Language Models
1799. ICML 2026On the Interaction of Batch Noise, Adaptivity, and Compression, under $(L_0,L_1)$-Smoothness: An SDE Approach
1800. ICML 2026On the Interplay of Pre-Training, Mid-Training, and RL on Reasoning Language Models

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.