RL Exploration

RL: Reinforcement Learning

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Overview

Exploration studies how an agent can gather useful experience when rewards are sparse, the environment is uncertain, or its current policy visits only a narrow part of the state space. Common approaches use uncertainty bonuses, novelty and entropy rewards, model-based planning, goal or skill discovery, memory, and structured or language-guided exploration. These ideas are tested in settings from bandits and sparse-reward navigation to robotics, multi-agent tasks, and language-model reasoning and tool use. A central challenge is to seek information that improves future decisions without wasting experience on irrelevant novelty or destabilizing learning, while maintaining safety and useful behavior.

Latest papers

CardsList

221 papers29 papers in the last four weeks, up 81% on the four weeks before. 0.2% of all new papers.

  1. Poly-EPO: Training Exploratory Reasoning Models

    Apr 19, 2026Ifdita Hasan Orney, Jubayer Ibn Hamid, Shreya S Ramanujam +5Reinforcement LearningRL for Language Model Reasoning

    Stanford University

  2. SPS: Steering Probability Squeezing for Better Exploration in Reinforcement Learning for Large Language Models

    Apr 18, 2026Yifu Huo, Chenglong Wang, Ziming Zhu +9Reinforcement LearningRL for Language Model Reasoning

    1Northeastern University, Shenyang, China · 2CAS Key Laboratory of Behavioral Science, Beijing, China · 3Independent Researcher, Beijing, China +1

  3. Flexible Empowerment at Reasoning with Extended Best-of-N Sampling

    Apr 17, 2026Taisuke KobayashiReinforcement LearningBest-of-N Sampling

    National Institute of Informatics and The Graduate University for Advanced Studies, SOKENDAI Japan

  4. Targeted Exploration via Unified Entropy Control for Reinforcement Learning

    Apr 16, 2026Chen Wang, Lai Wei, Yanzhi Zhang +5Reinforcement LearningRL for Language Model Reasoning

    College of Software, Nankai University · Zhongguancun Academy · Shanghai Jiao Tong University +3

  5. DiPO: Disentangled Perplexity Policy Optimization for Fine-grained Exploration-Exploitation Trade-Off

    Apr 15, 2026Xiaofan Li, Ming Yang, Zhiyuan Ma +9Reinforcement LearningRL for Language Model Reasoning

    East China Normal University · Ant Group · Xiamen University +1

  6. Learning-Guided Sparsification of Dynamic Graphs in Robotic Exploration

    Apr 15, 2026Adithya V. Sastry, Bibek Poudel, Weizi LiGraph SparsificationMobile Robotics

    Independent Researcher Knoxville, TN, USA · Purdue University West Lafayette, IN, USA · University of California, Riverside Riverside, CA, USA

  7. Policy Split: Incentivizing Dual-Mode Exploration in LLM Reinforcement with Dual-Mode Entropy Regularization

    Apr 13, 2026Jiashu Yao, Heyan Huang, Daiqing Wu +2RL for Language ModelsReinforcement Learning

    Beijing Institute of Technology · Tsinghua University · Beihang University

  8. ExploreVLA: Dense World Modeling and Exploration for End-to-End Autonomous Driving

    Apr 3, 2026Zihao Sheng, Xin Ye, Jingru Luo +2World Model LearningVLMs for Autonomous Driving

    Bosch Research North America & Bosch Center for Artificial Intelligence (BCAI) · University of Wisconsin–Madison

  9. ProCeedRL: Process Critic with Exploratory Demonstration Reinforcement Learning for LLM Agentic Reasoning

    Apr 2, 2026Jingyue Gao, Yanjiang Guo, Xiaoshuai Chen +1Agentic RLRL for Language Model Reasoning

    Institute for Interdisciplinary Information Sciences, Tsinghua University · Shanghai Qizhi Institute · Independent Researcher

  10. Optimistic Online LQR via Intrinsic Rewards

    Mar 30, 2026Marcell Bartos, Bruce D. Lee, Lenart Treven +3Linear Quadratic RegulatorRegret Minimization in RL

    ETH Zürich, Zürich, Switzerland

  11. Match or Replay: Self Imitating Proximal Policy Optimization

    Mar 29, 2026Gaurav Chaudhary, Laxmidhar Behera, Washim Uddin MondalRL ExplorationSample-Efficient RL

    Department of Electrical Engineering, Indian Institute of Technology Kanpur

  12. Where-to-Learn: Analytical Policy Gradient Directed Exploration for On-Policy Robotic Reinforcement Learning

    Mar 28, 2026Leixin Chang, Xinchen Yao, Ben Liu +2RL ExplorationRobotic RL

  13. 120 Minutes and a Laptop: Minimalist Image-goal Navigation via Unsupervised Exploration and Offline RL

    Mar 27, 2026Xiaoming Liu, Borong Zhang, Qingbiao Li +1Robot NavigationImage-Goal Navigation

    Faculty of Science and Technology, University of Macau

  14. Maximum Entropy Behavior Exploration for Sim2Real Zero-Shot Reinforcement Learning

    Mar 26, 2026Jiajun Hu, Nuria Armengol Urpi, Jin Cheng +1Zero-Shot RLReinforcement Learning

    Department of Mechanical Engineering, EPFL · Department of Computer Science, ETH Zurich · Max Planck Institute for Intelligent Systems

  15. Learning Rollout from Sampling:An R1-Style Tokenized Traffic Simulation Model

    Mar 26, 2026Ziyan Wang, Peng Chen, Ding Li +4Autonomous Driving SimulationReinforcement Learning

    State Key Laboratory of Intelligent Transportation System, Key Laboratory of Autonomous Transportation Technology for Special Vehicles, Ministry of Industry and Information Technology, School of Transportation Science and Engineering, Beihang University, Beijing 100191, China. · State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.

  16. Off-Policy Safe Reinforcement Learning with Constrained Optimistic Exploration

    Mar 25, 2026Guopeng Li, Matthijs T. J. Spaan, Julian F. P. KooijSafe RLConstrained RL

    Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, the Netherlands

  17. Decoupling Exploration and Policy Optimization: Uncertainty Guided Tree Search for Hard Exploration

    Mar 23, 2026Zakaria Mhammedi, James CohanUncertainty-Aware ExplorationRL Exploration

    Google Research, NYC

  18. Manifold-Aware Exploration for Reinforcement Learning in Video Generation

    Mar 23, 2026Mingzhe Zheng, Weijie Kong, Yue Wu +9RL for Video GenerationRL Exploration

    HKUST · Tencent Hunyuan

  19. Experience is the Best Teacher: Motivating Effective Exploration in Reinforcement Learning for LLMs

    Mar 20, 2026Wenjian Zhang, Kongcheng Zhang, Jiaxin Qi +2Hindsight Experience ReplayRL for Language Model Reasoning

    Dalian University of Technology · Zhejiang University · Chinese Academy of Sciences

  20. Maximum-Entropy Exploration with Future State-Action Visitation Measures

    Mar 19, 2026Adrien Bolland, Gaspard Lambrechts, Damien ErnstRL ExplorationIntrinsic Reward Methods

    University of Liège

  21. Escaping Offline Pessimism: Vector-Field Reward Shaping for Safe Frontier Exploration

    Mar 18, 2026Amirhossein Roknilamouki, Arnob Ghosh, Eylem Ekici +1RL ExplorationReward Design for RL

    Department of Electrical and Computer Engineering, The Ohio State University · Department of Electrical and Computer Engineering, New Jersey Institute of Technology · Department of Computer Science and Engineering, The Ohio State University.

  22. Efficient Soft Actor-Critic with LLM-Based Action-Level Guidance for Continuous Control

    Mar 18, 2026Hao Ma, Zhiqiang Pu, Xiaolin Ai +1LLM-Guided RLRL Exploration

    School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China · Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China · JD.com, Beijing, China

  23. Efficient Exploration at Scale

    Mar 18, 2026Seyed Mohammad Asghari, Chris Chute, Vikranth Dwaracherla +5RL from Human FeedbackData-Efficient Learning

    The Efficient Agent Team, Google DeepMind

  24. Stochastic Resetting Accelerates Reinforcement Learning Beyond Random Search

    Mar 17, 2026Jello Zhou, David J. Schwab, Vudtiwat NgampruetikornRL Exploration

    Biophysics Program, Stanford University, Stanford, California 94301, USA · Initiative for the Theoretical Sciences and Princeton-CUNY Center for the Physics of Biological Function, The Graduate Center, CUNY, New York, NY 10016, USA · School of Physics, University of Sydney, Sydney, NSW 2006, Australia +1

  25. Emergent Dexterity via Diverse Resets and Large-Scale Reinforcement Learning

    Mar 16, 2026Patrick Yin, Tyler Westenbroek, Zhengyu Zhang +9Robotic RLRobotic Manipulation

    University of Washington · NVIDIA · Microsoft Research

  26. How Log-Barrier Helps Exploration in Policy Optimization

    Mar 16, 2026Leonardo Cesani, Matteo Papini, Marcello RestelliRL ExplorationPolicy Gradient Methods

    Politecnico di Milano, Milan, Italy · Università degli Studi di Milano, Milan, Italy

  27. Maximum Entropy Exploration Without the Rollouts

    Mar 12, 2026Jacob Adamczyk, Adam Kamoski, Rahul V. KulkarniRL ExplorationMaximum Entropy RL

    Department of Physics, University of Massachusetts Boston · NSF Institute for Artificial Intelligence and Fundamental Interactions

  28. Meta-Reinforcement Learning with Self-Reflection for Agentic Search

    Mar 11, 2026Teng Xiao, Yige Yuan, Hamish Ivison +6Reinforcement LearningAgentic Search

    Allen Institute for AI · University of Washington · Independent

  29. RIE-Greedy: Regularization-Induced Exploration for Contextual Bandits

    Mar 11, 2026Tong Li, Thiago de Queiroz Casanova, Eric M. Schwartz +3Contextual BanditsExploration-Exploitation Tradeoff

    University of Toronto Canada · Braze United States · University of Michigan United States

  30. ContactExplorer: Contact Coverage-Guided Exploration for General-Purpose Dexterous Manipulation

    Mar 11, 2026Zixuan Liu, Ruoyi Qiao, Chenrui Tie +5Contact-Rich Robotic ManipulationRL Exploration

    School of Computing, National University of Singapore · RoboScience