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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Multi-Agent Reinforcement Learning from Delayed Marketplace Feedback for Objective-Weight Adaptation in Three-Sided Dispatch
Jun 11, 2026Haochen Wu, Yi Hou, Shiguang XieMulti-Objective Reinforcement Learning Reinforcement Learning
DoorDash Inc.
Individual Control Barrier Functions-Guided Diffusion Model for Safe Offline Multi-Agent Reinforcement Learning
Jun 10, 2026Qingyun Guo, Junyi Shi, Jianuo Huang +1Control Barrier Functions Offline RL
Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland · School of Computing and Data Science, Xiamen University Malaysia, Sepang, Malaysia · Department of Computer Science, University of Toronto, Toronto, Canada
Test-Time Gradient Guidance of Flow Policies in Reinforcement Learning
Jun 9, 2026Zhiyuan Zhou, Andy Peng, Charles Xu +4Policy Optimization Offline RL
UC Berkeley · Physical Intelligence
Fast and Highly Expressive Policy Learning for Offline Reinforcement Learning via Bootstrapped Flow Q-Learning
Jun 9, 2026Thanh Nguyen, Tri Ton, Hongbin Choe +2Flow Matching Q-Learning
Department of Electrical Engineering, KAIST, Daejeon, Korea.
Safe-RULE: Safe Reinforcement UnLEarning
Jun 8, 2026Shixiong Jiang, Taozheng Zhu, Fanxin KongOffline RL Safe RL
Department of Computer Science and Engineering, University of Notre Dame
Goal Sets, Not Goal States: Queryable Robot Goals through Goal-Set Hindsight Relabeling
Jun 8, 2026Carlos Vélez García, Miguel Cazorla, Jorge PomaresOffline RL Goal-Conditioned RL
INESCOP · University of Alicante
Counterfactual Transport Flows for Offline Conservative Trajectory Refinement
Jun 8, 2026Lena Krieger, Xuan Zhao, Zhuo Cao +3Trajectory Optimization Offline RL
1IAS-8, Forschungszentrum J ulich, Germany · 2LMU Munich, Munich Center for Machine Learning (MCML), Germany · Department of Computer Science, Aarhus University, Denmark.
Neuro-Symbolic Injection of LTLf Constraints in Autoregressive Reinforcement Learning Policies
Jun 6, 2026Ashkan Ansarifard, Matteo Mancanelli, Elena Umili +1Reinforcement Learning Transformer-Based RL
Sapienza University of Rome · University of Rome La Sapienza, Rome, Italy
FlowPRO: Reward-Free Reinforced Fine-Tuning of Flow-Matching VLAs via Proximalized Preference Optimization
Jun 3, 2026Yihao Wu, He Zhang, Junbo Tan +2Offline RL Vision-Language-Action Models
Tencent Robotics X · Futian Laboratory · Tsinghua University
Generalizable Multi-Task Learning for Wireless Networks Using Prompt Decision Transformers
Jun 3, 2026Fatih Temiz, Shavbo Salehi, Melike Erol-KantarciReinforcement Learning Transformer
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada
Dual Advantage Fields
Jun 2, 2026Alexey Zemtsov, Maxim Bobrin, Alexander Nikulin +5Offline RL Goal-Conditioned RL
1NUST MISIS · 2MSU · 3Computational Imaging Lab +3
When Offline Selectors Cannot Beat the Best Single Model: A Diagnostic Study on edX Dropout Prediction
Jun 2, 2026Tyler Crosse, Alan Nadelsticher Ruvalcaba, Dustin Khang LeDuc +3Model Selection Educational Data Mining
Georgia Institute of Technology.
From Reward-Free Representations to Preferences: Rethinking Offline Preference-Based Reinforcement Learning
May 31, 2026Jun-Jie Yang, Chia-Heng Hsu, Kui-Yuan Chen +1Pairwise Preference Learning Representation Learning
Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
Interaction-Limited Safe Continuous-Time RL for Dynamical Medical Treatment
May 31, 2026Xun Shen, Yuepeng Wang, Akifumi Wachi +13Reinforcement Learning Sequential Decision Making
Tokyo University of Agriculture and Technology · LY Corporation · National University of Singapore +6
Behavior-Invariant Task Representation Learning with Transformer-based World Models for Offline Meta-Reinforcement Learning
May 30, 2026Fuyuan Qian, Menglong Zhang, Song Wang +1Representation Learning Offline RL
Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China · AI Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China · Omni-Intelligence, Shenzhen, Guangdong, China +1
Regularized Offline Policy Optimization with Posterior Hybrid Bayesian Belief
May 30, 2026Hongqiang Lin, Pengfei Wang, Nenggan ZhengReinforcement Learning Bayesian RL
Zhejiang University, Hangzhou, China.
Drift Q-Learning
May 29, 2026Anas Houssaini, Mohamad H. Danesh, Amin Abyaneh +3Reinforcement Learning Q-Learning
McGill University · Mila - Quebec AI Institute
DRIFT: Decoupled Rollouts and Importance-Weighted Fine-Tuning for Efficient Multi-Turn Optimization
May 29, 2026Jian Mu, Tianyi Lin, Chengwei Qin +2Supervised Fine-Tuning RL for Language Models
The Hong Kong University of Science and Technology (Guangzhou) · The Chinese University of Hong Kong, Shenzhen.
A Lecture Note on Offline RL and IRL, Part II: Foundations of Inverse Reinforcement Learning and Dynamic Discrete Choice Models
May 29, 2026Enoch Hyunwook KangReinforcement Learning Sequential Decision Making
University of Washington, Foster School of Business
UNIQ: Conformal Calibration for Adaptive Conservatism in Offline Reinforcement Learning
May 28, 2026Aditya UpadhyayOffline RL
IIIT Delhi
Return-to-Go Is More Than a Number: Q-Guided Alignment for Return-Conditioned Supervised Learning
May 27, 2026Yuxiao Yang, Weitong ZhangReinforcement Learning Decision Transformer
University of North Carolina at Chapel Hill
Efficient Post-training of LLMs for Code Generation With Offline Reinforcement Learning
May 27, 2026Mingze Wu, Abhinav Anand, Shweta Verma +1RL for Code Generation Offline RL
Technische Universitat, Darmstadt, Germany · Hessian Center for Artificial Intelligence, Darmstadt, Germany · National Research Center for Applied Cybersecurity ATHENE
Adaptive Coarse-to-Fine Subgoal Refinement for Long-Horizon Offline Goal-Conditioned Reinforcement Learning
May 27, 2026Kaiqiang Ke, Shenghong He, Chengdong Xu +3Hierarchical RL Offline RL
1Sun Yat-sen University · 2Pengcheng Laboratory
SPAR: Support-Preserving Action Rectification
May 27, 2026Jiaxin Zhao, Weihang Pan, Xun Liang +1Imitation Learning Offline RL
Zhejiang University.
Trust Region Q Adjoint Matching
May 26, 2026Yonghoon Dong, Kyungmin Lee, Changyeon Kim +2Reinforcement Learning KL-Regularized RL
KAIST AI · Seoul National University · RLWRLD
Exploiting Local Dynamics Regularity for Reusable Skills in Offline Hierarchical RL
May 25, 2026Sarthak Dayal, Abhinav Peri, Carl Qi +4Hierarchical RL Offline RL
Department of Computer Science University of Texas at Austin · UT Austin · OpenAI
Latent Representation Alignment for Offline Goal-Conditioned Reinforcement Learning
May 25, 2026Hyungkyu Kang, Byeongchan Kim, Min-hwan OhReinforcement Learning Offline RL
Seoul National University, Seoul, South Korea.
Back to Parsimonious Latents: Learning Task-Centric World Models from Visual Foundations
May 25, 2026Minghao Fu, Fan Feng, Nicklas Hansen +1World Model Learning World Models
University of California, San Diego
SeqRoute: Global Budget-Aware Sequential LLM Routing via Offline Reinforcement Learning
May 25, 2026Zhongling Xu, Shunan Zheng, Wei WangCost-Aware Inference Offline RL
Department of Operations Research and Industrial Engineering The University of Texas at Austin Austin, TX 78712
Unifying Value Alignment and Assignment in Cross-Domain Offline Reinforcement Learning with Heterogeneous Datasets
May 24, 2026Zhongjian Qiao, Jiafei Lyu, Chenjia Bai +3Offline RL Cross-Domain Transfer Learning
City University of Hong Kong · Tencent · Institute of Artificial Intelligence (TeleAI), China Telecom +1

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Multi-Agent Reinforcement Learning from Delayed Marketplace Feedback for Objective-Weight Adaptation in Three-Sided Dispatch

Haochen Wu, Yi Hou, Shiguang Xie

DoorDash Inc.

Multi-Objective Reinforcement Learning Reinforcement Learning Offline RL

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Dispatch in three-sided marketplaces provides a natural setting for reinforcement learning from world feedback: decisions are evaluated by delayed operational outcomes such as delivery speed, courier utilization, and merchant congestion. We present a deployed reinforcement learning system at DoorDash that adapts dispatch objective weights in a large-scale food-delivery marketplace using delayed signals. Rather than replacing the combinatorial assignment optimizer, a store-level policy learned from logged marketplace data selects a discrete multiplier that shifts the dispatch optimizer's tradeoff between delivery quality and batching efficiency. This interface enables offline policy learning under noisy, delayed, and coupled feedback while preserving production feasibility constraints and operational safeguards. We train a shared value function using centralized offline data and decentralized store-level execution, with Double Q-learning targets and a conservative regularizer to reduce out-of-distribution value overestimation. In a production switchback experiment, the offline-trained policy increases batching and reduces courier-side time costs without degrading customer-facing delivery quality. Results illustrate how world feedback from a live economic and logistics system can be used to safely adapt decision policies online.

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Latest papers 189