Sample-Efficient RL

RL: Reinforcement Learning

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12 papers in the last four weeks, up 140% on the four weeks before. 0.1% of all new papers.

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Reflex: Reinforcement Learning with Reflection Symmetry Exploitation in State-Based Continuous Control
May 22, 2026Shuai Zhen, Yifan Zhang, Yuling Wang +1RL Control Reinforcement Learning
Beijing University of Posts and Telecommunications · Hangzhou Dianzi University
Pure Exploration for a Good Policy in Reinforcement Learning with Bandit Feedback
May 22, 2026Zitian Li, Wang Chi CheungReinforcement Learning RL Exploration
Department of Industrial Systems Engineering & Management National University of Singapore Engineering Drive 2 Block E1A #06-25 Singapore 117576
Dreaming Smoothly and Sample Efficiently with Gradient Penalized Latent Dynamics
May 21, 2026Romil V. Sonigra, P. R. KumarLatent Dynamics Modeling Jacobian Regularization
Texas A&M University · Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843
On the Sample Complexity of Discounted Reinforcement Learning with Optimized Certainty Equivalents
May 20, 2026Oliver Mortensen, Mohammad Sadegh TalebiReinforcement Learning Risk-Sensitive RL
Department of Computer Science, University of Copenhagen.
Reinforcement Learning-based Control via Y-wise Affine Neural Networks: Comparative Case Studies for Chemical Processes
May 20, 2026Austin Braniff, Yuhe TianRL Control Feedback Control
Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV 26506 USA
ReversedQ: Opportunities for Faster Q-Learning in Episodic Online Reinforcement Learning
May 20, 2026Sofia R. Miskala-Dinc, Aviva PrinsReinforcement Learning Q-Learning
University of Maryland College Park, MD, United States
Sampling-Based Safe Reinforcement Learning
May 19, 2026Luca Vignola, Bruce D. Lee, Manish Prajapat +4Robot Safety Safe RL
ETH Zurich
When to Stop Reusing: Dynamic Gradient Gating for Sample-Efficient RLVR
May 19, 2026Yuchun Miao, Sen Zhang, Yuqi Zhang +4RL for Language Model Reasoning Policy Optimization
National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University · Meituan Longcat Team · The University of Sydney +1
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning
May 18, 2026Ehsan Ahmadi, Hunter Schofield, Behzad Khamidehi +5Multi-Agent Systems Autonomous Driving Simulation
University of Alberta · 2Huawei Technologies Canada · 3York University +1
COOPO: Cyclic Offline-Online Policy Optimization Algorithm
May 18, 2026Qisai Liu, Zhanhong Jiang, Joshua Russell Waite +3Offline RL Sample-Efficient RL
Department of Mechanical Engineering, Iowa State University, Ames, IA 50011, USA. · Department of Computer Science, Iowa State University, Ames, IA 50011, USA. · Translational AI Center, Iowa State University, Ames, IA 50011, USA.
Privacy Preserving Reinforcement Learning with One-Sided Feedback
May 18, 2026Lin William Cong, Guangyan Gan, Hanzhang Qin +1Reinforcement Learning Partially Observable RL
1Nanyang Technological University · Cornell SC Johnson College of Business · 2National University of Singapore
EfficientTDMPC: Improved MPC Objectives for Sample-Efficient Continuous Control
May 15, 2026Thomas Evers, Cristian Meo, Wendelin Bohmer +2Model Predictive Control Continuous Control
TU Delft · LatentWorlds AI
Boosting Reinforcement Learning with Verifiable Rewards via Randomly Selected Few-Shot Guidance
May 14, 2026Kai Yan, Alexander G. Schwing, Yu-Xiong WangRL for Language Model Reasoning Reinforcement Learning with Verifiable Rewards
University of Illinois Urbana-Champaign
Addressing Terminal Constraints in Data-Driven Demand Response Scheduling
May 14, 2026Maximilian Bloor, Martha White, Ehecatl Antonio del Rio Chanona +1Hierarchical RL Credit Assignment in RL
Sargent Centre for Process Systems Engineering, Imperial College London, London, SW7 2AZ, UK · Department of Computer Science, University of Alberta, Edmonton, AB, Canada · Department of Computing, Imperial College London, London, SW7 2AZ, UK
R2R2: Robust Representation for Intensive Experience Reuse via Redundancy Reduction in Self-Predictive Learning
May 13, 2026Sanghyeob Song, Donghyeok Lee, Jinsik Kim +1Reinforcement Learning Self-Supervised Learning
Interdisciplinary Program in Artificial Intelligence, Seoul National University · Samsung Electro-Mechanics, Republic of Korea · Department of Electrical and Computer Engineering, Seoul National University
Tight Sample Complexity Bounds for Entropic Best Policy Identification
May 13, 2026Amer Essakine, Claire VernadeReinforcement Learning Risk-Sensitive RL
ENS Paris Saclay · University of Technology Nuremberg
Achieving $ε^{-2}$ Sample Complexity for Single-Loop Actor-Critic under Minimal Assumptions
May 13, 2026Ishaq Hamza, Zaiwei ChenPolicy Gradient Last-Iterate Convergence
IISc · Edwardson School of Industrial Engineering, Purdue University · Purdue IE
Learning with Rare Success but Rich Feedback via Reflection-Enhanced Self-Distillation
May 12, 2026Yuwei Zhang, Sha Li, Changlong Yu +9Continual Learning for LLMs Self-Distillation
UC San Diego1 · Amazon2 · Georgia Institute of Technology3
TMRL: Diffusion Timestep-Modulated Pretraining Enables Exploration for Efficient Policy Finetuning
May 12, 2026Matthew M. Hong, Jesse Zhang, Anusha Nagabandi +1Robot Policy Adaptation Robot Skill Learning
University of Washington · Amazon FAR
Debiased Model-based Representations for Sample-efficient Continuous Control
May 12, 2026Jiafei Lyu, Zichuan Lin, Scott Fujimoto +5Representation Learning Continuous Control
1Tencent Hunyuan · 2McGill University · School of Computer
XQCfD: Accelerating Fast Actor-Critic Algorithms with Prior Data and Prior Policies
May 11, 2026Daniel Palenicek, Florian Vogt, Joe Watson +3Robotic RL Learning from Demonstration
Technical University of Darmstadt · KTH Royal Institute of Technology · University of Oxford +2
When Does Non-Uniform Replay Matter in Reinforcement Learning?
May 11, 2026Michal Korniak, Mikołaj Czarnecki, Yarden As +3Experience Replay Sample-Efficient RL
1ETH Zurich · University of Warsaw · 3UC Berkeley +1
The Value of Mechanistic Priors in Sequential Decision Making
May 11, 2026Itai Shufaro, Gal Benor, Shie MannorMulti-Armed Bandits Sequential Decision Making
Technion · Technion, NVIDIA Research
From Passive Reuse to Active Reasoning: Grounding Large Language Models for Neuro-Symbolic Experience Replay
May 10, 2026Yanan Xiao, Yixiang Tang, Zechen Feng +3Reinforcement Learning Experience Replay
1Affiliation 1 · 2Affiliation 2 · 3Affiliation 3
DARE: Difficulty-Adaptive Reinforcement Learning with Co-Evolved Difficulty Estimation
May 9, 2026Yang Zhou, Can Jin, Zihan Dong +7Reinforcement Learning RL for Language Model Reasoning
Rutgers University · Amazon · Washington University +1
Actor-Critic with Active Importance Sampling
May 8, 2026Majid Molaei, Gabor Paczolay, Matteo Papini +2Reinforcement Learning Policy Gradient Methods
Politecnico di Milano Milan, Italy
Beyond the Independence Assumption: Finite-Sample Guarantees for Deep Q-Learning under $τ$ -Mixing
May 7, 2026Leon Halgryn, Sophie Langer, Janusz M. Meylahn +1Reinforcement Learning Deep Q-Learning
University of Twente · Department of Applied Mathematics University of Twente · Ruhr-Universität Bochum +1
Beyond Uniform Credit Assignment: Selective Eligibility Traces for RLVR
May 7, 2026Chaoli Mou, Zhan Zhuang, Xinning Chen +1RL for Language Model Reasoning Credit Assignment in RL
Southern University of Science and Technology · City University of Hong Kong
A Measure-Theoretic Finite-Sample Theory for Adaptive-Data Fitted Q-Iteration
May 7, 2026Manuel Haussmann, Mustafa Mert Çelikok, Melih KandemirReinforcement Learning Q-Learning
Department of Mathematics and Computer Science University of Southern Denmark
Adaptive Policy Selection and Fine-Tuning under Interaction Budgets for Offline-to-Online Reinforcement Learning
May 6, 2026Alper Kamil Bozkurt, Xiaoan Xu, Shangtong Zhang +2Sequential Decision Making RL Fine-Tuning
Virginia Commonwealth University, Richmond, VA · Duke University, Durham, NC · University of Virginia, Charlottesville, VA

May 22, 2026, cs.LGJ/K move · Enter open · S save

Reflex: Reinforcement Learning with Reflection Symmetry Exploitation in State-Based Continuous Control

Shuai Zhen, Yifan Zhang, Yuling Wang, Yanhua Yu

Beijing University of Posts and Telecommunications · Hangzhou Dianzi University

RL Control Reinforcement Learning Continuous Control Sample-Efficient RL

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Reinforcement learning has long struggled with poor sample efficiency. One promising approach to mitigate this problem is leveraging group-invariant Markov Decision Processes (

G

-invariant MDPs). Existing works in this direction have primarily focused on image-based RL and rotational symmetry such as

\mathrm{SO(2)}

, leaving state-based RL and reflection symmetry largely underexplored. In this work, we focus on state-based continuous control tasks and exploit reflection symmetry by introducing Reflex, a paradigm that seamlessly integrates with both on-policy and off-policy RL algorithms. We formalize two types of reflection-axial reflection and bilateral reflection, and characterize their corresponding transformations. Building on a theoretical analysis of symmetry-preserving optimal value functions and policies, Reflex integrates reflection symmetry into policy learning through principled symmetry regularization mechanisms. We integrate Reflex with PPO and SAC, and evaluate it on a suite of OpenAI Gym and DeepMind Control benchmarks, demonstrating superior performance over standard baselines while improving sample efficiency. Our code is available at https://github.com/TonyStark042/Reflex.

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