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 57 of 60

2801. NeurIPS 2026SAMPPO: Structure-Aware Mirror Proximal Policy Optimization
2802. NeurIPS 2026Scalable Minimal-Change Learning for Controllable Image Editing
2803. NeurIPS 2026Scalable Multi-Agent Contrastive Reinforcement Learning
2804. NeurIPS 2026SceneFactory: GPU-Accelerated Multi-Agent Driving Simulation with Physics-Based Vehicle Dynamics
2805. NeurIPS 2026Scratchpad Patching: Decoupling Compute from Patch Size in Byte-Level Language Models
2806. NeurIPS 2026SD-Search: Hindsight Self-Distillation for Search-Augmented Reasoning
2807. NeurIPS 2026SEAR: Sample Efficient Action Chunking Reinforcement Learning
2808. NeurIPS 2026Seeing but Not Detecting: Privacy-Preserving Scene Text Attack via Hybrid Adversarial Policy Learning
2809. NeurIPS 2026Selective Off-Policy Reference Tuning with Plan Guidance
2810. NeurIPS 2026Self-Calibrated GUI Reward Model via Inverse Dynamic Modeling
2811. NeurIPS 2026Self-Correction as Transition Geometry: Internalizing Reasoning via Lifted State Policy Optimization
2812. NeurIPS 2026Self-Distilled RLVR
2813. NeurIPS 2026Self-Evolution Reasoner: Continuous Optimization via Policy-Intrinsic Exploration and Exploitation
2814. NeurIPS 2026Self-Evolving Agents Should Build Internal and External Models of the World
2815. NeurIPS 2026Self-Improvement Imitation with Biologically Guided Search for Protein Design Under Oracle Budgets
2816. NeurIPS 2026Self-Supervised On-Policy Reinforcement Learning via Contrastive Proximal Policy Optimisation
2817. NeurIPS 2026SelfCritic-VLA: Language as Intrinsic Critic for Vision Language Action Models in Autonomous Driving
2818. NeurIPS 2026Serialization Tax in Shared-Latent Exchangeable Decisions
2819. NeurIPS 2026SETA: Scaling Environments for Terminal Agents
2820. NeurIPS 2026SFT-then-RL Outperforms Mixed-Policy Methods for LLM Reasoning
2821. NeurIPS 2026Shared Modular Recurrence in Contextual MDPs for Universal Morphology Control
2822. NeurIPS 2026Sharper Guarantees for Misspecified Kernelized Bandit Optimization
2823. NeurIPS 2026Signal-Adaptive Trust Regions for Gradient-Free Optimization of Recurrent Spiking Neural Networks
2824. NeurIPS 2026Simulating Students or Sycophantic Problem Solving? On Misconception Faithfulness of LLM Simulators
2825. NeurIPS 2026Simulation-aided Reinforcement Learning with Control Variates
2826. NeurIPS 2026SIRAS: Sibling-Relative Advantage Shaping for Reinforcement Learning from Verifiable Rewards
2827. NeurIPS 2026Skill-Coupled Policy Optimization with Calibrated Group-Wise Advantage Estimation
2828. NeurIPS 2026Skill-SD: Skill-Conditioned Self-Distillation for Multi-turn LLM Agents
2829. NeurIPS 2026SkillForge: Co-Evolving Skills and Agents via Dynamic Skill Lifecycles
2830. NeurIPS 2026SLOPE: Optimistic Potential Landscape Shaping for Model-based Reinforcement Learning
2831. NeurIPS 2026Smart Picks in the Dark: Towards Efficient RLVR for Reasoning via Tracing Metacognitive Pivots
2832. NeurIPS 2026SOC-ICNN: From Polyhedral to Conic Geometry for Learning Convex Surrogate Functions
2833. NeurIPS 2026Soft Forward-Backward Representations for Zero-shot Reinforcement Learning with General Utilities
2834. NeurIPS 2026SOLE-R1: Video-Language Reasoning as the Sole Reward for On-Robot Reinforcement Learning
2835. NeurIPS 2026Spatial-IQ: Deconstructing Spatial Intelligence via Hierarchical Capability Tests
2836. NeurIPS 2026SpatialFlow-GRPO: Where Spatial Credit Drives Image Editing
2837. NeurIPS 2026SPECS: Faster Test-Time Scaling through Speculative Drafts and Dynamic Switching
2838. NeurIPS 2026Spectral Geometry of Attention: From Information Routing to Uncertainty
2839. NeurIPS 2026Spectral Representations for Provably Robust Offline Meta-Reinforcement Learning from Bagged Rewards
2840. NeurIPS 2026Spiking neural network initialization for scale-invariant maximization of entropy
2841. NeurIPS 2026Split the Differences, Pool the Rest: Provably Efficient Multi-Objective Imitation
2842. NeurIPS 2026Split-RL: Local Conflict Resolution in Reinforcement Learning
2843. NeurIPS 2026SPRING: Solver-guided Process Rewards for Novel Logical Reasoning Steps Generation
2844. NeurIPS 2026SR-GRPO: Stable Rank as an Intrinsic Geometric Reward for Large Language Model Alignment
2845. NeurIPS 2026SRL-MPC: Shape-Aware Reinforcement Learned Model Predictive Control
2846. NeurIPS 2026Stabilizing Knowledge, Promoting Reasoning: Dual-Token Constraints for RLVR
2847. NeurIPS 2026Stabilizing Policy Optimization via Logits Convexity
2848. NeurIPS 2026Stable and Granular Policy Optimization for Generative Recommendation
2849. NeurIPS 2026StainNFT: Curriculum-Gated Multi-Reward Post-Training for Pathology-Faithful Virtual Staining
2850. NeurIPS 2026STAPO: Stabilizing Reinforcement Learning for LLMs by Silencing Rare Spurious Tokens

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.