Sample-Efficient RL

RL: Reinforcement Learning

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Overview

Sample-efficient reinforcement learning aims to reduce the experience needed to learn useful policies, especially when interaction is slow, costly, or risky. Common approaches improve exploration with optimism or uncertainty estimates, reuse data through replay and demonstrations, learn models for planning, or exploit structure such as hierarchy, symmetry, and known dynamics. These methods are studied in settings from tabular and robust decision processes to MuJoCo and Atari control, robotics, and language-model training with verifiable rewards. The field balances empirical gains and PAC or regret guarantees against challenges such as stale replay, model error, limited data coverage, and differences between sample counts and wall-clock cost.

Latest papers

CardsList

253 papers30 papers in the last four weeks, up 150% on the four weeks before. 0.2% of all new papers.

  1. Enhancing sample efficiency in reinforcement-learning-based flow control: replacing the critic with an adaptive reduced-order model

    Apr 5, 2026Zesheng Yao, Zhen-Hua Wan, Canjun Yang +2Sample-Efficient RLModel-Based RL

    School of Mechanical Engineering, Zhejiang University, Hangzhou, PR China · Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, 117575 Singapore · Department of Modern Mechanics, University of Science and Technology of China, Hefei 230027, PR China

  2. Model-Based Learning of Near-Optimal Finite-Window Policies in POMDPs

    Apr 1, 2026Philip Jordan, Maryam KamgarpourPartially Observable Markov Decision ProcessesPolicy Learning

    SYCAMORE group, Institute of Mechanical Engineering, EPFL Switzerland.

  3. Optimistic Actor-Critic with Parametric Policies for Linear Markov Decision Processes

    Mar 30, 2026Max Qiushi Lin, Reza Asad, Kevin Tan +3Actor-Critic MethodsPolicy Learning

    Simon Fraser University · University of Pennsylvania · Mila, McGill University +1

  4. 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

  5. Rainbow-DemoRL: Combining Improvements in Demonstration-Augmented Reinforcement Learning

    Mar 28, 2026Dwait Bhatt, Shih-Chieh Chou, Nikolay AtanasovOffline-To-Online RLSample-Efficient RL

    Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA · Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Taiwan

  6. Unsupervised Behavioral Compression: Learning Low-Dimensional Policy Manifolds through State-Occupancy Matching

    Mar 27, 2026Andrea Fraschini, Davide Tenedini, Riccardo Zamboni +2RL ControlDistribution Matching

    Politecnico di Milano · Technion

  7. PiCSRL: Physics-Informed Contextual Spectral Reinforcement Learning

    Mar 26, 2026Mitra Nasr Azadani, Syed Usama Imtiaz, Nasrin AlamdariContextual RLAdaptive Sensing

    Department of Civil and Environmental Engineering Florida State University, Tallahassee, FL, USA

  8. Train at Moving Edge: Online-Verified Prompt Selection for Efficient RL Training of Large Reasoning Model

    Mar 26, 2026Jiahao Wu, Ning Lu, Shengcai Liu +6Prompt Selection for RLRL for Language Models

    Southern University of Science and Technology · The Hong Kong Polytechnic University · The Hong Kong University of Science and Technology +3

  9. End-to-End Efficient RL for Linear Bellman Complete MDPs with Deterministic Transitions

    Mar 24, 2026Zakaria Mhammedi, Alexander Rakhlin, Nneka OkoloReinforcement LearningLinear Markov Decision Processes

    Google Research, NYC · MIT

  10. Bitboard version of Tetris AI

    Mar 24, 2026Xingguo Chen, Pingshou Xiong, Zhenyu Luo +6Game-Playing AgentsRL Benchmarks

    School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, 210023, China

  11. Delightful Distributed Policy Gradient

    Mar 20, 2026Ian OsbandPolicy Gradient MethodsPolicy Learning

    Google DeepMind

  12. Breaking the Capability Ceiling of LLM Post-Training by Reintroducing Markov States

    Mar 20, 2026Yurun Yuan, Tengyang XieRL for Language Model ReasoningRL Post-Training

    UW-Madison

  13. Evaluating Model-Free Policy Optimization in Masked-Action Environments via an Exact Blackjack Oracle

    Mar 19, 2026Kevin SongPolicy OptimizationReinforcement Learning

    Department of Biomedical Engineering The University of Alabama at Birmingham

  14. Discounted Beta-Bernoulli Reward Estimation for Sample-Efficient Reinforcement Learning with Verifiable Rewards

    Mar 19, 2026Haechan Kim, Soohyun Ryu, Gyouk Chu +2Reinforcement LearningRL for Language Model Reasoning

    Kim Jaechul Graduate School of AI, KAIST, Daejeon, South Korea · KRAFTON, Seoul, South Korea · AITRICS, Seoul, South Korea

  15. Complementary RL: Towards Efficient Experience-Driven Agent Learning

    Mar 18, 2026Dilxat Muhtar, Jiashun Liu, Wei Gao +8Reinforcement LearningSample-Efficient RL

    Alibaba Group · HKUST

  16. 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

  17. Game-Theory-Assisted Reinforcement Learning for Border Defense: Early Termination based on Analytical Solutions

    Mar 16, 2026Goutam Das, Michael Dorothy, Kyle Volle +1Game-Theoretic RLReinforcement Learning

    School of Aeronautics and Astronautics, Purdue University, 610 Purdue Mall, West Lafayette, IN 47907, USA · Army Research Directorate, DEVCOM Army Research Laboratory, APG, MD 21005, USA · Munitions Directorate, Air Force Research Laboratory, Eglin AFB, FL 32542, USA +1

  18. Counteractive RL: Rethinking Core Principles for Efficient and Scalable Deep Reinforcement Learning

    Mar 16, 2026Ezgi KorkmazReinforcement LearningSample-Efficient RL

  19. Sample-Efficient Hypergradient Estimation for Decentralized Bi-Level Reinforcement Learning

    Mar 16, 2026Mikoto Kudo, Takumi Tanabe, Akifumi Wachi +1Hypergradient OptimizationDecentralized MARL

    University of Tsukuba · RIKEN AIP · LY Corporation

  20. Scaling Reasoning Efficiently via Relaxed On-Policy Distillation

    Mar 11, 2026Jongwoo Ko, Sara Abdali, Young Jin Kim +2On-Policy DistillationRL for Language Model Reasoning

  21. From Prior to Pro: Efficient Skill Mastery via Distribution Contractive RL Finetuning

    Mar 10, 2026Zhanyi Sun, Shuran SongRL for RoboticsRobot Policy Learning

    1Stanford University.

  22. SPAARS: Safer RL Policy Alignment through Abstract Exploration and Refined Exploitation of Action Space

    Mar 10, 2026Swaminathan S K, Aritra HazraReinforcement LearningOffline-To-Online RL

    Department of Computer Science and Engineering, IIT Kharagpur

  23. Breaking the Bias Barrier in Concave Multi-Objective Reinforcement Learning

    Mar 9, 2026Swetha Ganesh, Vaneet AggarwalNatural Policy GradientMulti-Objective Reinforcement Learning

    Purdue University, USA 47907

  24. Robust Transfer Learning with Side Information

    Mar 9, 2026Akram S. Awad, Shihab Ahmed, Yue Wang +1Reinforcement LearningRobust Markov Decision Processes

    Department of Electrical and Computer Engineering, University of Central Florida, USA · Department of Computer Science, University of Central Florida, USA

  25. Optimistic Policy Regularization: Elite-Statistic Reward Shaping

    Mar 6, 2026Mai Pham, Vikrant Vaze, Peter ChinRegularized RLSample-Efficient RL

    Thayer School of Engineering, Dartmouth College, USA

  26. Rethinking the Efficiency and Effectiveness of Reinforcement Learning for Radiology Report Generation

    Mar 4, 2026Zilin Lu, Ruifeng Yuan, Weiwei Cao +6Radiology Report GenerationSample-Efficient RL

    DAMO Academy, Alibaba Group · Ningbo No.2 Hospital, China · Hupan Lab, 310023, China +2

  27. GIPO: Gaussian Importance Sampling Policy Optimization

    Mar 4, 2026Chengxuan Lu, Zhenquan Zhang, Shukuan Wang +3Reinforcement LearningImportance Sampling for RL

    1IROOTECH TECHNOLOGY · 2Wolf 1069 b Lab, Sany Group

  28. A Covering Framework for Offline POMDPs Learning using Belief Space Metric

    Mar 3, 2026Youheng Zhu, Yiping LuOff-Policy EvaluationOffline RL

    Northwestern University

  29. Learning in Markov Decision Processes with Exogenous Dynamics

    Mar 3, 2026Davide Maran, Davide Salaorni, Marcello RestelliMarkov Decision ProcessesSample-Efficient RL

    Department of DEIB, Politecnico di Milano, Milan, Italy.

  30. Generalized Per-Agent Advantage Estimation for Multi-Agent Policy Optimization

    Mar 3, 2026Seongmin Kim, Giseung Park, Woojun Kim +3Multi-Agent Reinforcement LearningSample-Efficient RL

    KAIST Daejeon, Korea · University of Toronto Toronto, Ontario, Canada · Carnegie Mellon University Pittsburgh, Pennsylvania, USA +1