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

1,640 papers, page 28 of 33

1351. NeurIPS 2025Prompted Policy Search: Reinforcement Learning through Linguistic and Numerical Reasoning in LLMs
1352. NeurIPS 2025ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models
1353. NeurIPS 2025ProtoPairNet: Interpretable Regression through Prototypical Pair Reasoning
1354. NeurIPS 2025Provable Ordering and Continuity in Vision-Language Pretraining for Generalizable Embodied Agents
1355. NeurIPS 2025Provably Efficient Online RLHF with One-Pass Reward Modeling
1356. NeurIPS 2025Provably Efficient RL under Episode-Wise Safety in Constrained MDPs with Linear Function Approximation
1357. NeurIPS 2025Proxy Target: Bridging the Gap Between Discrete Spiking Neural Networks and Continuous Control
1358. NeurIPS 2025PurpCode: Reasoning for Safer Code Generation
1359. NeurIPS 2025Q-Insight: Understanding Image Quality via Visual Reinforcement Learning
1360. NeurIPS 2025QiMeng-CodeV-R1: Reasoning-Enhanced Verilog Generation
1361. NeurIPS 2025QiMeng-SALV: Signal-Aware Learning for Verilog Code Generation
1362. NeurIPS 2025QoQ-Med: Building Multimodal Clinical Foundation Models with Domain-Aware GRPO Training
1363. NeurIPS 2025Quality-Driven Curation of Remote Sensing Vision-Language Data via Learned Scoring Models
1364. NeurIPS 2025Quantifying Generalisation in Imitation Learning
1365. NeurIPS 2025Quantile Reward Policy Optimization: Alignment with Pointwise Regression and Exact Partition Functions
1366. NeurIPS 2025Quantization-Free Autoregressive Action Transformer
1367. NeurIPS 2025R$^2$ec: Towards Large Recommender Models with Reasoning
1368. NeurIPS 2025R1-ShareVL: Incentivizing Reasoning Capabilities of Multimodal Large Language Models via Share-GRPO
1369. NeurIPS 2025RAD: Training an End-to-End Driving Policy via Large-Scale 3DGS-based Reinforcement Learning
1370. NeurIPS 2025Rainbow Delay Compensation: A Multi-Agent Reinforcement Learning Framework for Mitigating Observation Delays
1371. NeurIPS 2025Ranking-based Preference Optimization for Diffusion Models from Implicit User Feedback
1372. NeurIPS 2025RAST: Reasoning Activation in LLMs via Small-model Transfer
1373. NeurIPS 2025Raw2Drive: Reinforcement Learning with Aligned World Models for End-to-End Autonomous Driving (in CARLA v2)
1374. NeurIPS 2025RDD: Retrieval-Based Demonstration Decomposer for Planner Alignment in Long-Horizon Tasks
1375. NeurIPS 2025ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding
1376. NeurIPS 2025Real-DRL: Teach and Learn in Reality
1377. NeurIPS 2025Real-Time Execution of Action Chunking Flow Policies
1378. NeurIPS 2025Real-World Adverse Weather Image Restoration via Dual-Level Reinforcement Learning with High-Quality Cold Start
1379. NeurIPS 2025Real-World Reinforcement Learning of Active Perception Behaviors
1380. NeurIPS 2025Reason-RFT: Reinforcement Fine-Tuning for Visual Reasoning of Vision Language Models
1381. NeurIPS 2025ReasonFlux-PRM: Trajectory-Aware PRMs for Long Chain-of-Thought Reasoning in LLMs
1382. NeurIPS 2025Reasoning as an Adaptive Defense for Safety
1383. NeurIPS 2025Reasoning Gym: Reasoning Environments for Reinforcement Learning with Verifiable Rewards
1384. NeurIPS 2025Reasoning Models Hallucinate More: Factuality-Aware Reinforcement Learning for Large Reasoning Models
1385. NeurIPS 2025Reasoning Models Sometimes Output Illegible Chains of Thought
1386. NeurIPS 2025Rebalancing Return Coverage for Conditional Sequence Modeling in Offline Reinforcement Learning
1387. NeurIPS 2025Rectifying Shortcut Behaviors in Preference-based Reward Learning
1388. NeurIPS 2025ReDit: Reward Dithering for Improved LLM Policy Optimization
1389. NeurIPS 2025Reducing the Probability of Undesirable Outputs in Language Models Using Probabilistic Inference
1390. NeurIPS 2025Regret Analysis of Average-Reward Unichain MDPs via an Actor-Critic Approach
1391. NeurIPS 2025Regret Lower Bounds for Decentralized Multi-Agent Stochastic Shortest Path Problems
1392. NeurIPS 2025Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learning
1393. NeurIPS 2025ReinFlow: Fine-tuning Flow Matching Policy with Online Reinforcement Learning
1394. NeurIPS 2025REINFORCE Converges to Optimal Policies with Any Learning Rate
1395. NeurIPS 2025Reinforced Active Learning for Large-Scale Virtual Screening with Learnable Policy Model
1396. NeurIPS 2025Reinforced Context Order Recovery for Adaptive Reasoning and Planning
1397. NeurIPS 2025Reinforcement Learning Finetunes Small Subnetworks in Large Language Models
1398. NeurIPS 2025Reinforcement learning for one-shot DAG scheduling with comparability identification and dense reward
1399. NeurIPS 2025Reinforcement Learning for Out-of-Distribution Reasoning in LLMs: An Empirical Study on Diagnosis-Related Group Coding
1400. NeurIPS 2025Reinforcement Learning for Reasoning in Large Language Models with One Training Example

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.