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

760 papers, page 3 of 16

101. NeurIPS 2026CLIFT: Conformal Self-Verification for Web Agent Training and Test-Time Scaling
102. NeurIPS 2026Co-Evolving Policy Distillation
103. NeurIPS 2026Co-GRPO: Co-Optimized Group Relative Policy Optimization for Masked Image Generation
104. NeurIPS 2026Code2World: A GUI World Model via Renderable Code Generation
105. NeurIPS 2026CodeScaler: Scaling Code LLM Training and Test-Time Inference via Reward Models
106. NeurIPS 2026CoE-Agent: Co-Evolving Patient-Doctor Agents via Interactive Policy Graph Optimization for Clinical Decision Making
107. NeurIPS 2026Cognitive Firewalls: A Synthetic Account of Cross-Lingual Reasoning Collapse
108. NeurIPS 2026COMPASS: Composable Policy-Amortized Structured Search for LLM-Based Optimization Modeling
109. NeurIPS 2026CompilerKV: Risk-Adaptive KV Cache Compression via Offline Experience Compilation
110. NeurIPS 2026Compose Your Oracles: Off Policy Improvement with Aggregated Guidance
111. NeurIPS 2026Composition-RL: Compose Your Verifiable Prompts for Reinforcement Learning of Large Language Models
112. NeurIPS 2026Compute Aligned Training: Optimizing for Test Time Inference
113. NeurIPS 2026Conclusions from Circuit Extraction Depend on the Level of Description: A Controlled Comparison
114. NeurIPS 2026Conformal Selective Acting: Anytime-Valid Risk Control for RLVR-Trained LLMs
115. NeurIPS 2026Conservatism Controllable Compositional Guidance for Offline Safe Reinforcement Learning
116. NeurIPS 2026Conservative Pareto Set Amortization for Offline Multi-Objective Optimization
117. NeurIPS 2026Constrained Goal-directed Planar Graph Generation with Grammar-based Reinforcement Learning
118. NeurIPS 2026Context Value Informed In-Context Reinforcement Learning
119. NeurIPS 2026Control First, Robustness Next: Decoupled Representation Learning for Visual RL Generalization
120. NeurIPS 2026Controllable Molecular Generative Foundation Models
121. NeurIPS 2026Controllable Multi-label Video Safety Detection via Adaptive Tversky Policy Optimization
122. NeurIPS 2026Cooperative Multi-Agent Reinforcement Learning via Epigraph-Form Guided Exploration
123. NeurIPS 2026Coordinating Hundreds of RL Agents through Scalable Inference-Time Search
124. NeurIPS 2026Coordination Connectivity: Shared Initialization Shapes the Joint-Policy Landscape in MARL
125. NeurIPS 2026Cornerstones or Stumbling Blocks? Deciphering the Rock Tokens in On-Policy Distillation
126. NeurIPS 2026Correct, Route, Calibrate: Efficient Preference Optimization from Noisy, Heterogeneous Human Feedback
127. NeurIPS 2026Correctable Fork Tokens: Verifier-Anchored Selective Credit Assignment for Tool-Integrated RLVR
128. NeurIPS 2026Cost-Aware Learning
129. NeurIPS 2026CoT-Guard: Small Models for Strong Monitoring
130. NeurIPS 2026Counterfactual Distillation: Internalizing Reflective Experience into LLM Agent
131. NeurIPS 2026Counterfactual Rollout Replay: Forkable Environments as Free Process Rewards for Software Engineering Agents
132. NeurIPS 2026Coupling-Aware Reinforcement Learning for Co-Evolving Graph Games
133. NeurIPS 2026CPA: Efficient and Stable FP4 RL Training via Cross-Precision Alignment
134. NeurIPS 2026CRAFT: Counterfactual-to-Interactive Reinforcement Fine-Tuning for Driving Policies
135. NeurIPS 2026Credit Assignment with Resets in Language Model Reasoning
136. NeurIPS 2026Cross-Question Reliable Reinforcement Learning
137. NeurIPS 2026CS-DICE: Offline Reinforcement Learning with Coherent Occupancy Regularization
138. NeurIPS 2026CURe: Conservative Unlearning with Soft-Gating Regularization for Offline Reinforcement Learning
139. NeurIPS 2026CURE: Coupled User-Grouped Reinforcement Learning for Cross-Domain Recommendation with Non-Overlapping Users
140. NeurIPS 2026Curriculum proof repair with learned counterexamples
141. NeurIPS 2026CurveRL: Principled Distribution-Aware Context Reweighting for LLM Reasoning
142. NeurIPS 2026CyCLeGen: Cycle-Consistent Layout Prediction and Image Generation
143. NeurIPS 2026D-VLA: A High-Concurrency Distributed Asynchronous RL Framework for Vision-Language-Action Models
144. NeurIPS 2026DACA-GRPO: Denoising-Aware Credit Assignment for Reinforcement Learning in Diffusion Language Models
145. NeurIPS 2026DACE RL for Compute Efficient Reinforcement Learning in Small Model Reasoning
146. NeurIPS 2026DaCe-DT: Data-Centric Offline Multi-Task Reinforcement Learning via Adaptive Prompts and Trajectory Correction for Heterogeneous Tasks
147. NeurIPS 2026DARLING: Detection Augmented Reinforcement Learning with Non-Stationary Guarantees
148. NeurIPS 2026DASM: Dynamic Autoregressive Subgraph Mining via Two-Stage Policy Alignment
149. NeurIPS 2026DDACOM: Dual-Decoupled Adaptive Communication for Multi-Agent Reinforcement Learning under Dynamic Networks
150. NeurIPS 2026Deadline-Constrained Dynamic Workflow Scheduling Can be Cast as a Representation Learning Problem

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.