Flow-Based Policy Learning

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37 papers10 papers in the last four weeks, up 233% on the four weeks before. 0.1% of all new papers.

From Suboptimal to Optimal: One-Step Latent Flow Policies for Offline Reinforcement Learning
Oct 12, 2026Shenghua Lu, Zixuan Yang, Rongxu Zhao +3Offline RL Flow-Based Policy Learning
Jilin University
DEFlow: Deep Equilibrium Flow Maximum-Entropy Reinforcement Learning
Oct 12, 2026Ruslan Ageev, Huaze Tang, Runqing Miao +2Flow-Based Policy Learning Reinforcement Learning
Tsinghua University · Peng Cheng Laboratory
$Δ$ Flow Matching for Robot Foundation Models
Oct 11, 2026Kuangji Zuo, Tuo An, Xinying Guo +9Flow Matching for Action Generation Robot Foundation Models
MARS Lab, Nanyang Technological University
Reinforcement Learning via Fixed-point Flow Matching
Oct 9, 2026Denis Blessing, Onur Celik, Gerhard NeumannFlow-Based Policy Learning Reinforcement Learning
Karlsruhe Institute of Technology (KIT) Autonomous Learning Robots Lab (ALR)
RFPO: Rectified Flow Policy Optimization for Embodied Control
Oct 7, 2026Ting Huang, Lisiyu Pan, Haoyu Wang +6Robot Policy Learning Flow-Based Policy Learning
School of Computer Science, Peking University · AI2 Robotics
QF3: Fast Flow RL with Filtered Q-Gradients
Oct 6, 2026Chung Min Kim, Brent Yi, David McAllister +7Robotic RL RL for Legged Locomotion
UC Berkeley · Amazon FAR
Diffusion Policy Improvement with Proposal-Conditioned Refinement Flows
Sep 29, 2026Junhyun Ha, Juho Lee, Byoungwoo ParkPolicy Optimization Offline RL
KAIST
QAMM: Adjoint MeanFlow Matching for Few-Step Offline Reinforcement Learning
Sep 28, 2026Yuehu Gong, Shutong Ding, Mokai Pan +4Flow Matching MeanFlow
School of Data Science, Fudan University · ShanghaiTech University · Shanghai Innovation Institute
Constrained Flow Policy Updates: A Generalized Schrödinger Bridge View
Sep 26, 2026Boyang Li, Matthew Kim, Sylvia HerbertFlow-Based Policy Learning Reinforcement Learning
University of California San Diego
MA-FPPO: Multi-Agent Flow-Pretrained Policy Optimization
Sep 26, 2026Guowei Zou, Haonan Chen, Haitao Wang +3Cooperative MARL Multi-Agent Coordination
Sun Yat-sen University · National University of Singapore
Fewer Steps, Better Actions: Rethinking Flow-Matching Inference for VLA Policies
Sep 18, 2026Zhipeng Tang, Xinda Chen, Weining Rao +5Flow-Based VLA Models Efficient VLA Model Inference
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China · University of Chinese Academy of Sciences, Beijing, China · Zhongke Haichuan Intelligent
Learning Multimodal One-step Flow Policy via Value-weighted Optimal Transport
Sep 14, 2026Jaehun Shon, Jinha Choi, Jongwook Jeon +1Offline RL Flow-Based Policy Learning
Department of Artificial Intelligence, Yonsei University
Refinement-Based Flow Policy Optimization
Sep 14, 2026Bumgeun Park, Hyukjun Yang, Donghwan LeeRL Control Flow Matching
Korea Advanced Institute of Science and Technology (KAIST)
RLMM-Flow: A Flow-based Mobile Manipulation Framework with Latent-Space Reinforcement Learning
Jul 29, 2026Shuhang Wang, Ziming Li, Hui ChengReinforcement Learning Mobile Manipulation
$π\mathbf{R}^2$ : Reactive Real-time Flow Policies
Jul 28, 2026Sungjae Park, Shubham TulsianiVisuomotor Policy Learning Flow Matching
Carnegie Mellon University
Optimal Transport Q-Learning for Flow Policy Steering and Acceleration
Jul 7, 2026Andreas Sochopoulos, Esmeralda S. Whitammer, Nikolaos Tsagkas +3Robotic RL Policy Learning
University of Edinburgh · 2Honda Research Institute Europe
FlowDPG: Deterministic Policy Gradient on Flow Matching Policies for Real-World Manipulation
Jun 21, 2026Kexin Shi, Junyao Shi, Poorvi Hebbar +5Deep Deterministic Policy Gradient Policy Gradient
Skild AI · Carnegie Mellon University · University of Pennsylvania
BayesFP: Posterior Estimation for Flow-Based Policies via Feynman-Kac Sampling
Jun 19, 2026Sreevardhan Sirigiri, Weiming Zhi, Fabio RamosDiffusion Policy Trajectory Generation
School of Computer Science, The University of Sydney, Australia · Australian Centre For Robotics, The University of Sydney, Australia · College of Connected Computing, Vanderbilt University, TN, USA. +1
Reversal Q-Learning
Jun 16, 2026Aditya Oberai, Seohong Park, Sergey LevineOff-Policy RL Flow-Based Policy Learning
University of California, Berkeley.
QPILOTS: Efficient Test-Time Q-Steering for Flow Policies
Jun 11, 2026Yifan Ruan, Chenyang Cao, Andreas Burger +7Test-Time RL Offline-To-Online RL
University of Toronto · Vector Institute · LG Electronics
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.
FLAG: Flow Policy MaxEnt-RL by Latent Augmented Guidance
May 29, 2026Sungha Kim, Gawon Lee, Jusuk Lee +3RL Control Maximum Entropy RL
Seoul National University · Georgia Institute of Technology
Score-Based One-step MeanFlow Policy Optimization
May 22, 2026Kyungyoon Kim, Donghyeon Ki, Hee-Jun Ahn +1Flow Matching MeanFlow
Korea University, Decision Making Lab · Gauss Labs Inc.
FLASH: Efficient Visuomotor Policy via Sparse Sampling
May 15, 2026Jiaqi Bai, Jindou Jia, Yuxuan Hu +5Flow Matching Long-Horizon Robotic Manipulation
MARS Lab, Nanyang Technological University, Singapore
Q-Flow: Stable and Expressive Reinforcement Learning with Flow-Based Policy
May 13, 2026JaeHyeok Doo, Byeongguk Jeon, Seonghyeon Ye +2Reinforcement Learning Flow-Based Policy Learning
1KAIST
Aligning Flow Map Policies with Optimal Q-Guidance
May 12, 2026Christos Ziakas, Alessandra Russo, Avishek Joey BoseFlow-Based Generative Modeling Offline-To-Online RL
Imperial College London · Mila
Mean Flow Policy Optimization
Apr 16, 2026Xiaoyi Dong, Xi Sheryl Zhang, Jian ChengMeanFlow Mean Flow
C2DL, Institute of Automation, Chinese Academy of Sciences · School of Artificial Intelligence, University of Chinese Academy of Sciences · AiRiA +1
ScoRe-Flow: Complete Distributional Control via Score-Based Reinforcement Learning for Flow Matching
Apr 13, 2026Xiaotian Qiu, Lukai Chen, Jinhao Li +3RL for Flow Matching Models Flow-Based Policy Learning
Zhejiang University, Hangzhou, China · Shanghai Innovation Institute · Shanghai Jiao Tong University, Shanghai, China +1
FlowRL: A Taxonomy and Modular Framework for Reinforcement Learning with Diffusion Policies
Mar 29, 2026Chenxiao Gao, Edward Chen, Tianyi Chen +1Flow-Based Policy Learning Diffusion Policy
Georgia Institute of Technology
HiFlow: Tokenization-Free Scale-Wise Autoregressive Policy Learning via Flow Matching
Mar 28, 2026Daichi Yashima, Koki Seno, Shuhei Kurita +2Visuomotor Policy Learning Flow-Based Policy Learning
Keio University · National Institute of Informatics Research and Development Center for Large Language Models · National Institute of Informatics

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From Suboptimal to Optimal: One-Step Latent Flow Policies for Offline Reinforcement Learning

Shenghua Lu, Zixuan Yang, Rongxu Zhao, Bohao Qu+2

Jilin University

Offline RL Flow-Based Policy Learning Latent Action Learning

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Offline reinforcement learning aims to learn effective policies from fixed datasets without environment interaction. Although generative models such as diffusion and flow matching enhance policy expressiveness, they typically start from uninformative Gaussian noise and generate actions through multi-step iteration, leading to high computational costs and failing to effectively leverage suboptimal trajectories in datasets. We propose MeanFlow2Better (MF2B), which reformulates offline RL as generative decision chunk optimization from current performance status to better performance status in a high-level latent decision space. MF2B maps trajectory chunks to high-level latent decision representations through an encoder, placing generation starting points within a structured decision space sculpted by behavioral data. The model learns a shared conditional mean velocity field in this space, constructing two flow mappings: from Gaussian-Induced Decision Sources toward positive behavioral decision regions, and from suboptimal decisions toward high-value decision regions. Both flow mappings complete decision refinement through one-step ODE integration over the unit time interval

Δt=1

, requiring neither multi-step iterative sampling nor backpropagation through time. Furthermore, we propose hierarchical chunk-level advantage to guide the flow direction toward high-return regions, where the high-level decisions cover multiple low-level actions, leading to sequence length compression from

T

K \approx T/H

and significantly lower computational overhead. On the D4RL Gym-MuJoCo benchmark, MF2B achieves an average score of 90.91, matching or surpassing strong baselines.