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 59 of 60

2901. NeurIPS 2026There are Levels to It: Red Teaming LLMs with Hierarchical Reinforcement Learning
2902. NeurIPS 2026Thinking with Images as Continuous Policy: Numerical Visual Chain-of-Thought
2903. NeurIPS 2026ThinkSafe: Self-Generated Safety Alignment for Reasoning Models
2904. NeurIPS 2026TIGER: Bridging the Multimodal Reasoning-Access Gap via Modality Counterfactuals
2905. NeurIPS 2026Tool Verification for Test-Time Reinforcement Learning
2906. NeurIPS 2026ToolSearcher: Optimizing Tool Selection at Scale via Reinforcement Learning
2907. NeurIPS 2026Topic-Aware Contextual Cascading Bandits
2908. NeurIPS 2026TOPPO: Rethinking PPO for Multi-Task Reinforcement Learning with Critic Balancing
2909. NeurIPS 2026ToS: Tree-of-Skill Reinforcement Learning driven by Behavior Tree for Long-Horizon Manipulation
2910. NeurIPS 2026Toward Cognitive Supersensing in Multimodal Large Language Models
2911. NeurIPS 2026Toward Embodied World Agents via Embodied-Planning Dataset and Interactive World Models
2912. NeurIPS 2026Toward Executable Multi-framework Front-end Code Generation with Self-Correction
2913. NeurIPS 2026Toward Online Robust Zero-Sum Markov Games with Function Approximation
2914. NeurIPS 2026Towards Convergence of PPO: An Approximate Descent Approach
2915. NeurIPS 2026Towards Differentially Private Reinforcement Learning with General Function Approximation
2916. NeurIPS 2026Towards On-Policy Data Evolution for Visual-Native Multimodal Deep Search Agents
2917. NeurIPS 2026Towards Optimal Pre-training Data Mixtures for Empowering Reinforcement Learning in LLMs
2918. NeurIPS 2026TPRL: Adaptive Visual Token Pruning in LVLMs via Language-Guided Reinforcement Learning
2919. NeurIPS 2026Train at the Moving Edge: Rollout-Efficient RL for Large Reasoning Models
2920. NeurIPS 2026Train the Agent, Not the Expert: Learning to Harness Heterogeneous Experts for Multi-Turn Visual Reasoning
2921. NeurIPS 2026Training Agent to Scale Inference-Time Reasoning
2922. NeurIPS 2026Training Generalizable Collaborative Agents via Strategic Risk Aversion
2923. NeurIPS 2026Training on Documents About Monitoring Leads to CoT Obfuscation
2924. NeurIPS 2026Training-Induced Escape from Token Clustering in a Mean-Field Formulation of Transformers
2925. NeurIPS 2026Trajectory-Consistent Diffusion Policies for Offline Reinforcement Learning
2926. NeurIPS 2026Transfer Learning of Linear Regression with Multiple Pretrained Models: Benefiting from More Pretrained Models via Overparameterization Debiasing
2927. NeurIPS 2026Treat Bias as Noise: Training Bias-Robust LLM Reasoning via Reinforcement Learning
2928. NeurIPS 2026Tree Training: Efficient LLM Training on Tree-Structured Trajectories
2929. NeurIPS 2026Trust Guided Decision Transformer
2930. NeurIPS 2026Trust Region Q Adjoint Matching
2931. NeurIPS 2026TrustFlow: Adaptive Trust Calibration for Language Model Guided Reinforcement Learning
2932. NeurIPS 2026Tuning the Tuner
2933. NeurIPS 2026Ultra-DPO: Reimagining LLM Alignment as a Semi-Supervised Task
2934. NeurIPS 2026Uncertainty-Aware Listwise Reinforcement Fine-Tuning for Fine-Grained Visual Classification
2935. NeurIPS 2026Uncertainty-Guided Reward Labeling for Reinforcement Learning under Limited Feedback
2936. NeurIPS 2026Underlying Functional Structure of Reinforcement Learning
2937. NeurIPS 2026Uni-Synergy: Bridging Understanding and Generation for Personalized Reasoning
2938. NeurIPS 2026Unlearning Diffusion Policies via Relative Fisher Forgetting
2939. NeurIPS 2026Unleashing the Power of Intrinsic-Entropy-Driven Exploration for Off-Policy Generative RL
2940. NeurIPS 2026Unveiling Implicit Advantage Symmetry: Why GRPO Struggles with Exploration and Difficulty Adaptation
2941. NeurIPS 2026Value-Priced Uncertainty: A Family of PPO-Compatible Exploration Bonuses
2942. NeurIPS 2026Value-Rectified Distillation for Flow-based Offline Reinforcement Learning
2943. NeurIPS 2026Variance-Adaptive Optimal Algorithm for Reinforcement Learning with MNL Function Approximation
2944. NeurIPS 2026Variance-Averse n-Step Offline Reinforcement Learning for Sparse Long-Horizon Environments
2945. NeurIPS 2026Variational Consequence-Driven Offline Reinforcement Learning
2946. NeurIPS 2026VC-OPD: Visual Counterfactual On-Policy Distillation for Grounded Vision-Language Reasoning
2947. NeurIPS 2026Verifiable Environments Are LEGO Bricks: Recursive Composition for Reasoning Generalization
2948. NeurIPS 2026VESPO: Variational Sequence-level Soft Policy Optimization for Off-Policy LLM Training
2949. NeurIPS 2026VI-Bench: Benchmarking Prompt Inversion from AIGC Videos
2950. NeurIPS 2026VI-CuRL: Stabilizing Verifier-Independent RL Reasoning via Confidence-Guided Variance Reduction

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.