Offline RL

RL: Reinforcement Learning

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29 papers in the last four weeks, up 222% on the four weeks before. 0.3% of all new papers.

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Decoupled Guidance Diffusion for Adaptive Offline Safe Reinforcement Learning
May 4, 2026Rufeng Chen, Zhaofan Zhang, Zhejiang Yang +2Diffusion-Based Planning Diffusion Guidance
1The Hong Kong University of Science and Technology (Guangzhou) · 2Jilin University
On the Optimal Sample Complexity of Offline Multi-Armed Bandits with KL Regularization
May 4, 2026Kaixuan Ji, Qiwei Di, Heyang Zhao +2Multi-Armed Bandits Sample Complexity
Department of Computer Science, University of California, Los Angeles, CA 90095, USA
AdamO: A Collapse-Suppressed Optimizer for Offline RL
May 3, 2026Nan Qiao, Sheng Yue, Shuning Wang +1Reinforcement Learning Temporal-Difference Learning
1Tsinghua University, China · 2Central South University, China · 3Sun Yat-sen University, China
QHyer: Q-conditioned Hybrid Attention-mamba Transformer for Offline Goal-conditioned RL
May 3, 2026Xing Lei, Jincheng Wang, Xuetao Zhang +1Offline RL Sparse-Reward RL
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence and Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University · Computer Science, University College London · School of Engineering, Westlake University
Towards Efficient and Expressive Offline RL via Flow-Anchored Noise-conditioned Q-Learning
May 3, 2026Sungyoung Lee, Dohyeong Kim, Eshan Balachandar +2Distributional RL Q-Learning
The University of Texas at Austin, Austin, TX, USA · Independent Researcher, Seoul, South Korea.
Model-Based Proactive Cost Generation for Learning Safe Policies Offline with Limited Violation Data
May 2, 2026Ruiqi Xue, Lei Yuan, Kainuo Cheng +2LLM-Guided RL Offline RL
National Key Laboratory for Novel Software Technology, Nanjing University · School of Artificial Intelligence, Nanjing University · Polixir Technologies +2
Pessimism-Free Offline Learning in General-Sum Games via KL Regularization
Apr 30, 2026Claire Chen, Yuheng ZhangNash Equilibrium Reinforcement Learning
The Division of Physics, Mathematics and Astronomy California Institute of Technology · Department of Computer Science University of Illinois Urbana-Champaign
GUI Agents with Reinforcement Learning: Toward Digital Inhabitants
Apr 30, 2026Junan Hu, Jian Liu, Jingxiang Lai +6Reinforcement Learning RL for GUI Agents
1Shandong University · 2The Hong Kong University of Science and Technology · 3The Hong Kong University +2
Lyapunov-Guided Self-Alignment: Test-Time Adaptation for Offline Safe Reinforcement Learning
Apr 29, 2026Seungyub Han, Hyungjin Kim, Jungwoo LeeReinforcement Learning Offline RL
Seoul National University
TSN-Affinity: Similarity-Driven Parameter Reuse for Continual Offline Reinforcement Learning
Apr 28, 2026Dominik Żurek, Kamil Faber, Marcin Pietron +2Continual Learning Robotic RL
AGH University of Krakow, Poland · American University, Washington, DC, USA
Improving Zero-Shot Offline RL via Behavioral Task Sampling
Apr 28, 2026Nazim Bendib, Nicolas Perrin-Gilbert, Olivier SigaudReinforcement Learning Offline RL
Institut des Systèmes Intelligents et de Robotique, ISIR, Sorbonne Université, Paris, France.
DLM: Unified Decision Language Models for Offline Multi-Agent Sequential Decision Making
Apr 26, 2026Zhuohui Zhang, Bin Cheng, Bin HeSequential MARL Offline RL
Department of Control Science and Engineering, Tongji University, Shanghai 201804, China · Shanghai Institute of Intelligent Science and Technology; Shanghai Research Institute for Intelligent Autonomous Systems; State Key Laboratory of Autonomous Intelligent Unmanned Systems, Shanghai 200120, China
Learning from Demonstration with Failure Awareness for Safe Robot Navigation
Apr 25, 2026Xianghui Wang, Siwei Cheng, Shanze Wang +3Safe Robot Navigation Offline RL
Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong · College of Information Science and Technology, Eastern Institute of Technology, Ningbo, China · School of Computer Science and Technology, University of Science and Technology of China, Hefei, China +1
CODA: Coordination via On-Policy Diffusion for Multi-Agent Offline Reinforcement Learning
Apr 25, 2026Marcel Hedman, Kale-ab Abebe Tessera, Juan Claude Formanek +5Multi-Agent Coordination Offline RL
Inephany Ltd · Department of Statistics, University of Oxford · The University of Edinburgh +2
SOLAR-RL: Semi-Online Long-horizon Assignment Reinforcement Learning
Apr 24, 2026Jichao Wang, Liuyang Bian, Yufeng Zhou +9RL for GUI Agents Credit Assignment in RL
1vivo AI Lab · 2Zhejiang Lab · 5Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences +2
Preserve Support, Not Correspondence: Dynamic Routing for Offline Reinforcement Learning
Apr 24, 2026Zhancun Mu, Guangyu Zhao, Yiwu Zhong +1Reinforcement Learning Offline RL
School of Intelligence Science and Technology, Peking University
When Policies Cannot Be Retrained: A Unified Closed-Form View of Post-Training Steering in Offline Reinforcement Learning
Apr 23, 2026Elias Hossain, Mohammad Jahid Ibna Basher, Ivan Garibay +2Offline RL RL Post-Training
College of Engineering and Computer Science University of Central Florida, Orlando, FL, USA
Learning Dynamic Representations and Policies from Multimodal Clinical Time-Series with Informative Missingness
Apr 23, 2026Zihan Liang, Ziwen Pan, Ruoxuan XiongClinical Outcome Prediction Offline RL
Emory University, Atlanta, USA
Occupancy Reward Shaping: Improving Credit Assignment for Offline Goal-Conditioned Reinforcement Learning
Apr 22, 2026Aravind Venugopal, Jiayu Chen, Xudong Wu +3Reward Shaping Temporal Credit Assignment
Carnegie Mellon University · The University of Hong Kong · INFIFORCE Intelligent Technology +1
Lever: Inference-Time Policy Reuse under Support Constraints
Apr 22, 2026Ihor Vitenko, Noha Ibrahim, Sihem Amer-YahiaOffline RL Policy Learning
Anhalt University of Applied Sciences Germany · Université Grenoble Alpes Grenoble, France · CNRS, Univ. Grenoble Alpes Grenoble, France
Fisher Decorator: Refining Flow Policy via a Local Transport Map
Apr 20, 2026Xiaoyuan Cheng, Haoyu Wang, Wenxuan Yuan +4Flow Matching Offline RL
University College London · Shanghai Jiao Tong University · Imperial College London +3
GUIDE: Reinforcement Learning for Behavioral Action Support in Type 1 Diabetes
Apr 1, 2026Saman Khamesian, Sri Harini Balaji, Di Yang Shi +5Reinforcement Learning Type 1 Diabetes
College of Health Solutions, Arizona State University, Phoenix, USA · School of Computing and Augmented Intelligence, Arizona State University, Tempe, USA · School for Engineering of Matter Transport and Energy, Arizona State University, Tempe, USA +2
SutureFormer: Learning Surgical Trajectories via Goal-conditioned Offline RL in Pixel Space
Mar 19, 2026Huanrong Liu, Chunlin Tian, Tongyu Jia +8Offline RL Trajectory Prediction
Faculty of Science and Technology, University of Macau, Macau, China · University of Macau Advanced Research Institute in Hengqin, Zhuhai, China · Senior Department of Urology, The Chinese PLA General Hospital, Beijing, China +4
Safe Flow Q-Learning: Offline Safe Reinforcement Learning with Reachability-Based Flow Policies
Mar 16, 2026Mumuksh Tayal, Manan Tayal, Ravi PrakashFlow Matching Offline RL
Indian Institute of Science, India
Latent Policy Steering through One-Step Flow Policies
Mar 5, 2026Hokyun Im, Andrey Kolobov, Jianlong Fu +1RL for Robotics Latent Action Learning
Department of Artificial Intelligence, Yonsei University · Microsoft Research
Temporal Consistency Improves Generalization in Contextual Offline Meta Reinforcement Learning
Mar 3, 2026Mohammadreza Nakheai, Aidan Scannell, Kevin Luck +1Representation Learning Offline RL
Aalto University · Toyota Research Institute · Vrije Universiteit Amsterdam
Conservative Equilibrium Discovery in Offline Game-Theoretic Multiagent Reinforcement Learning
Feb 27, 2026Austin A. Nguyen, Michael P. WellmanReinforcement Learning Game Theory
University of Michigan
Beyond Success Rates: Trainability and Extractability for Offline GCRL
Feb 5, 2026Jan Malte Töpperwien, Aditya Mohan, Marius LindauerRL Benchmarks Offline RL
Institute of Artificial Intelligence (LUH|AI), Leibniz University Hannover · L3S Research Center
Chain-of-Goals Hierarchical Policy for Long-Horizon Offline Goal-Conditioned RL
Feb 3, 2026Jinwoo Choi, Sang-Hyun Lee, Seung-Woo SeoReinforcement Learning Hierarchical RL
Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea · Department of Automotive Engineering, Ajou University, Gyeonggi-do, South Korea
Offline Reinforcement Learning of High-Quality Behaviors Under Robust Style Alignment
Jan 30, 2026Mathieu Petitbois, Rémy Portelas, Sylvain LamprierReinforcement Learning Offline RL
Ubisoft La Forge, Bordeaux, France · Université d’Angers, Angers, France

May 4, 2026, cs.LGJ/K move · Enter open · S save

Decoupled Guidance Diffusion for Adaptive Offline Safe Reinforcement Learning

Rufeng Chen, Zhaofan Zhang, Zhejiang Yang, Hechang Chen+1

1The Hong Kong University of Science and Technology (Guangzhou) · 2Jilin University

Diffusion-Based Planning Diffusion Guidance Constrained RL Offline RL Safe RL

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Offline safe reinforcement learning often requires policies to adapt at deployment time to safety budgets that vary across episodes or change within a single episode. While diffusion-based planners enable flexible trajectory generation, existing guidance schemes often treat reward improvement and constraint satisfaction as competing gradient objectives, which can lead to unreliable safety compliance under cost limits. We reinterpret adaptive safe trajectory generation as sampling from a constrained trajectory distribution, where the budget restricts the trajectory region, and reward shapes preferences within that region. This perspective motivates Safe Decoupled Guidance Diffusion (SDGD), which conditions classifier-free guidance on the cost limit to bias sampling toward trajectories satisfying the specified limit, while using reward-gradient guidance to refine trajectories for higher return. Because direct reward guidance can increase return while also steering samples toward trajectories with higher cumulative cost, we introduce Feasible Trajectory Relabeling (FTR) to reshape reward targets and discourage such directions. We further provide a first-order sampling-time analysis showing that FTR suppresses reward-induced cost drift under a prefix-restorative alignment condition. Extensive evaluations on the DSRL benchmark show that SDGD achieves the strongest safety compliance among baselines, satisfying the constraint on 94.7% of tasks (36/38), while obtaining the highest reward among safe methods on 21 tasks.

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Latest papers 189