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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Agile Reinforcement Learning through Separable Neural Architecture and Applications
Jan 30, 2026Rajib Mostakim, Reza T. Batley, Sourav SahaRL Control Neural Network Approximation Theory
Department of Mechanical Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh · Kevin T. Crofton Department of Aerospace and Ocean Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
HeaPA: Difficulty-Aware Heap Sampling and On-Policy Query Augmentation for LLM Reinforcement Learning
Jan 30, 2026Weiqi Wang, Xin Liu, Binxuan Huang +13RL for Language Models RL for Language Model Reasoning
Stores Foundational AI, Amazon Inc., Palo Alto, CA, USA · Department of Computer Science and Engineering, HKUST, Hong Kong SAR, China
CLEANER: Self-Purified Trajectories Boost Agentic Reinforcement Learning
Jan 21, 2026Tianshi Xu, Yuteng Chen, Meng LiAgentic RL Reinforcement Learning
Peking University · NTU, Singapore
Reinforcement Learning in the Real World: A Survey of Statistical Challenges and Future Directions
Jan 21, 2026Asim H. Gazi, Yongyi Guo, Daiqi Gao +3Non-Stationary RL Reinforcement Learning
Department of Computer Science, Harvard University · Department of Statistics, Harvard University · Department of Statistics, University of Wisconsin–Madison +2
Auto-exploration for online reinforcement learning
Dec 6, 2025Caleb Ju, Guanghui LanPolicy Optimization RL Exploration
Agentic Episodic Control
Jun 2, 2025Xidong Yang, Wenhao Li, Junjie Sheng +4Agentic RL Reinforcement Learning
East China Normal University · Tongji University · Shanghai Jiao Tong University +2
Model-Free Robust Average-Reward Reinforcement Learning with Sample Complexity Analysis
May 18, 2025Zachary Roch, George Atia, Yue WangReinforcement Learning Average-Reward RL
Department of Electrical and Computer Engineering, University of Central Florida, Orlando, Florida, USA. · Department of Computer Science, University of Central Florida, Orlando, Florida, USA.
Adaptive Resolving Methods for Markov Decision Processes with Function Approximations
May 17, 2025Jiashuo Jiang, Yinyu Ye, Yiming ZongMarkov Decision Processes Reinforcement Learning
Department of Industrial Engineering & Decision Analytics, The Hong Kong University of Science and Technology · Department of Management Science & Engineering, Stanford University
Sample Complexity of Linear Quadratic Regulator Without Initial Stability
Feb 20, 2025Amirreza Neshaei Moghaddam, Alex Olshevsky, Bahman GharesifardRL Control Sample Complexity
Efficient Diversity-based Experience Replay for Deep Reinforcement Learning
Oct 27, 2024Kaiyan Zhao, Yiming Wang, Yuyang Chen +3Experience Replay Determinantal Point Process
School of Computer Science, Wuhan University, Wuhan, China · State Key Laboratory of Internet of Things for Smart City, University of Macau, Macao, China · School of Artificial Intelligence Shenzhen Polytechnic University, China
Adaptive teachers for amortized samplers
Oct 2, 2024Minsu Kim, Sanghyeok Choi, Taeyoung Yun +7Amortized Inference RL Exploration
Mila, KAIST · KAIST · Recursion +1
Exploiting Exogenous Structure for Sample-Efficient Reinforcement Learning
Sep 22, 2024Jia Wan, Sean R. Sinclair, Devavrat Shah +1Regret Minimization in RL Markov Decision Processes
Laboratory for Information and Decision Systems, Massachusetts Institute of Technology · Department of Industrial Engineering and Management Sciences, Northwestern University
ProSpec RL: Plan Ahead, then Execute
Jul 31, 2024Liangliang Liu, Yi Guan, BoRan Wang +5Reinforcement Learning Risk-Sensitive RL
1The Faculty of Computing, Harbin Institute of Technology, Harbin, China · 2The Artificial Intelligence Institute, Harbin Institute of Technology, ShenZhen, China · 3The Artificial Intelligence Institute, Harbin Institute of Technology, Harbin, China
Reflective Policy Optimization
Jun 6, 2024Yaozhong Gan, Renye Yan, Zhe Wu +1Policy Optimization Policy Gradient Methods
QiYuan Lab.
Provably Efficient Off-Policy Adversarial Imitation Learning with Convergence Guarantees
May 26, 2024Yilei Chen, Vittorio Giammarino, James Queeney +1Adversarial Imitation Learning Imitation Learning
Division of Systems Engineering Boston University Boston, MA 02215, USA · Department of Computer Science Purdue University West Lafayette, IN 47907, USA · Amazon Robotics North Reading, MA 01864, USA +1
Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithms
Apr 4, 2024Miao Lu, Han Zhong, Tong Zhang +1Distributionally Robust RL Robust RL
†Department of Management Science and Engineering, Stanford University. · ‡Center for Data Science, Peking University. · §Department of Computer Science, University of Illinois Urbana-Champaign.
ETHER: Aligning Emergent Communication for Hindsight Experience Replay
Jul 28, 2023Kevin Yandoka Denamganaï, Daniel Hernandez, Ozan Vardal +2Reinforcement Learning Experience Replay
Department of Computer Science University of York York, UK
Reinforcement Learning with Temporal-Logic-Based Causal Diagrams
Jun 23, 2023Yash Paliwal, Rajarshi Roy, Jean-Raphaël Gaglione +5Reinforcement Learning Causal RL
Arizona State University, Arizona, USA · Max Planck Institute for Software Systems, Kaiserslautern, Germany · University of Texas at Austin, Texas, USA +3

Jan 30, 2026, cs.LGJ/K move · Enter open · S save

Agile Reinforcement Learning through Separable Neural Architecture and Applications

Rajib Mostakim, Reza T. Batley, Sourav Saha

Department of Mechanical Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh · Kevin T. Crofton Department of Aerospace and Ocean Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA

RL Control Neural Network Approximation Theory Sample-Efficient RL Heating, Ventilation, and Air Conditioning Control

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Deep reinforcement learning (RL) is increasingly deployed in resource-constrained environments, yet go-to function approximators - multilayer perceptrons (MLPs) - are often parameter-inefficient due to an imperfect inductive bias for the smooth structure of many value functions. This mismatch can also hinder sample efficiency and slow policy learning in this capacity-limited regime. Although model compression techniques exist, they operate post-hoc and do not improve learning efficiency. Spline-based architectures such as Kolmogorov-Arnold Networks (KANs) have been shown to offer parameter efficiency but are widely reported to exhibit significant computational overhead, especially at scale. In seeking to address these limitations, this work introduces SPAN (SPline-based Adaptive Networks) for RL. SPAN adapts the KHRONOS framework with a learnable preprocessing layer. SPAN is evaluated across discrete (PPO) and high-dimensional continuous (SAC) control tasks, offline settings (Minari/D4RL) and a real-world datacenter HVAC control application. SPAN achieves a 30-50% improvement in sample efficiency and 1.3-9 times higher success rates across benchmarks compared to MLP baselines. Despite incurring a per-step evaluation overhead of 1.2-1.8x, SPAN's superior convergence reliability yields an expected total training cost 1.3-6.3x lower than MLP baselines when accounting for convergence failures. In the HVAC application, SPAN reduces energy consumption in 9 of 12 months relative to MLP while simultaneously achieving a 1.1-3.4x reduction in thermal comfort violations across the evaluation year, demonstrating generalization to real-world engineering control. Furthermore, SPAN demonstrates superior anytime performance and robustness to hyperparameter variations, suggesting it as a viable, high-performance alternative for learning efficient policies in resource-limited settings.

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