RL for Flow Matching Models

RL: Reinforcement Learning

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16 papers5 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Distribution-Based Regularization for Reward Fine-Tuning Diffusion and Flow Models
Oct 17, 2026Romeo Passaro, Jeongjae Lee, Jong Chul Ye +4Generative Modeling Diffusion Model Alignment
University of Oxford · AITHYRA · KAIST +2
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
MEND: RL For Flow Models via Proximal Velocity Matching
Oct 5, 2026Shreshth Saini, Neil Birkbeck, Yilin Wang +2Flow Matching RL for Image Generation
The University of Texas at Austin · Google · University of Colorado Boulder
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
Timestep Weighting: A Hidden Key to Effective ELBO-Based Flow-Matching RL
Sep 26, 2026Qinwei Ma, Jingzhe Shi, Simin Fan +2RL for Image Generation Adaptive Loss Weighting
College of AI, Tsinghua University · Department of Electrical and Computer Engineering, Princeton University · EPFL +1
LC-GRPO: Bridging Train-Inference Gap for Flow-Based GRPO with Langevin Correction
Aug 6, 2026Yingqing Guo, Hui Yuan, Zijian He +2RL for Video Generation RL for Image Generation
VINE: Taming Generative Control Policies for Reinforcement Learning
Jul 11, 2026Rushuai Yang, Zhuo Han, Houlin Li +10RL for Robotics Offline RL
1AgiBot · 3Peking University · 2The Hong Kong University of Science and Technology
ReFPO: Reflow Regularization for Flow Matching Policy Gradients
Jun 19, 2026Ge Wang, Yibo Peng, Fan Feng +10Policy Gradient Reinforcement Learning
Ising AI · CUHK-Shenzhen · FNii-Shenzhen
Reversal Q-Learning
Jun 16, 2026Aditya Oberai, Seohong Park, Sergey LevineOff-Policy RL Flow-Based Policy Learning
University of California, Berkeley.
Flow-DPPO: Divergence Proximal Policy Optimization for Flow Matching Models
Jun 9, 2026Bowen Ping, Xiangxin Zhou, Penghui Qi +3RL for Video Generation RL for Image Generation
Xi’an Jiaotong University · Tencent Hunyuan · National University of Singapore
Reinforcement Learning for Flow-Matching Policies with Density Transport
Jun 7, 2026Boshu Lei, Kostas Daniilidis, Antonio LoquercioFlow Matching Robotic RL
University of Pennsylvania · Philadelphia, PA 19103
AdvantageFlow: Regularized Advantage-Weighted RL in Flow Models
May 25, 2026Branislav Kveton, Anup Rao, Subhojyoti Mukherjee +2Rectified Flow RL for Image Generation
Adobe Research
Reinforce Adjoint Matching: Scaling RL Post-Training of Diffusion and Flow-Matching Models
May 11, 2026Andreas Bergmeister, Stefanie Jegelka, Nikolas Nüsken +2Diffusion Model Alignment Flow Matching
1TU Munich, MCML · 2MIT CSAIL · 3King’s College London +2
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
Discrete Flow Matching Policy Optimization
Apr 7, 2026Maojiang Su, Po-Chung Hsieh, Weimin Wu +4TV Regularization RL for Flow Matching Models
Center for Foundation Models and Generative AI, Northwestern University, Evanston, IL 60208, USA · Department of Electrical Engineering, National Taiwan University, Taipei 10617, Taiwan · Department of Physics, National Taiwan University, Taipei 10617, Taiwan +1
Alleviating Sparse Rewards by Modeling Step-Wise and Long-Term Sampling Effects in Flow-Based GRPO
Feb 6, 2026Yunze Tong, Mushui Liu, Canyu Zhao +7Flow Matching Temporal Credit Assignment
1Zhejiang University, Hangzhou, China · 2Taobao & Tmall Group of Alibaba, Hangzhou, China · 3Tsinghua University, Beijing, China

Oct 17, 2026J/K move · Enter open · S save

Distribution-Based Regularization for Reward Fine-Tuning Diffusion and Flow Models

Romeo Passaro, Jeongjae Lee, Jong Chul Ye, Michael M. Bronstein+3

University of Oxford · AITHYRA · KAIST+2

Generative Modeling Diffusion Model Alignment T2I Generation RL for Diffusion Models RL for Flow Matching Models

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Reward fine-tuning is an effective way to adapt pretrained diffusion and flow-matching models to downstream objectives, but aggressive optimization often leads to mode collapse and distributional drift. Existing methods attempt to control this drift with reverse-KL regularization, implemented as an

\ell_2

penalty on the velocity field; however, this functions by indirectly constraining sampling dynamics rather than directly acting on the generated distribution. To address this, we propose DFR (Distributional Functional Rewards), which augments the task reward with a distribution-dependent, per-sample reward that acts explicitly on the generated distribution. Formally, we generalize the KL-regularized objective from expected rewards, which are linear in the model's distribution, to convex functionals of it. The first variation of this objective defines an effective, distribution-dependent reward that directly penalizes deviation from the pretrained distribution. This formulation yields additive per-sample rewards for the Fréchet distance (FD), maximum mean discrepancy (MMD), and DreamSim diversity, recovering several recently proposed rewards as special cases. Across various text-to-image fine-tuning tasks, DFR matches the peak reward of KL-tuned baselines while increasing diversity by up to 29% at equal or lower drift.