arXiv Machine Learning June 11, 2026 By Zeyang Li, Sunbochen Tang, Navid Azizan

Reverse Flow Matching: A Unified Framework for Online Reinforcement Learning with Diffusion and Flow Policies

Read the original on arXiv Machine Learning →

arXiv:2601. 08136v2 Announce Type: replace Abstract: Diffusion and flow policies are gaining prominence in online reinforcement learning (RL) due to their expressive power, yet training them efficiently remains a critical challenge.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Aug 17

Designing Reinforcement Learning for Diffusion Models: A Unified Path-Space View

arXiv:2608. 14430v1 Announce Type: new Abstract: Reinforcement learning (RL) post-training provides a direct way to align diffusion models with human preferences and task-specific rewards.

By Yixian Xu, Yuanrui Zhang, Shengjie Luo, Liwei Wang, Di He

diffusion reinforcement-learning

arXiv AI

Sep 15

Refinement-based Flow Policy Optimization

arXiv:2609.15123v1 Announce Type: cross Abstract: Flow-based policies offer an expressive representation for online reinforcement learning, but conventional flow matching requires samples drawn from...

By Bumgeun Park, Hyukjun Yang, Donghwan Lee

diffusion reinforcement-learning multimodal

arXiv Machine Learning

Aug 31

Amortizing intractable inference in diffusion models for vision, language, and control

The paper introduces a data‑free learning objective called relative trajectory balance for training diffusion models to sample from a posterior defined by a diffusion prior and an arbitrary black‑box constraint or likelihood. It proves asymptotic correctness of this objective and demonstrates its use across vision, language, and multimodal tasks, including classifier guidance, language infilling, and text‑to‑image generation. Additionally, the method is applied to continuous control with a score‑based behavior prior, achieving state‑of‑the‑art results in offline reinforcement learning.

By Siddarth Venkatraman, Moksh Jain, Luca Scimeca, Minsu Kim, Marcin Sendera, Mohsin Hasan, Luke Rowe, Sarthak Mittal, Pablo Lemos, Emmanuel Bengio, Alexandre Adam, Jarrid Rector-Brooks, Yoshua Bengio, Glen Berseth, Esmeralda S. Whitammer

llms diffusion reinforcement-learning multimodal benchmarks

arXiv AI

Jun 9

Reinforcement Learning for Flow-Matching Policies with Density Transport

arXiv:2606. 08602v1 Announce Type: cross Abstract: We present an online reinforcement learning (RL) algorithm for fine-tuning flow-matching policies in continuous-control problems.

By Boshu Lei, Kostas Daniilidis, Antonio Loquercio

diffusion reinforcement-learning robotics fine-tuning efficiency multimodal safety

arXiv Machine Learning

Sep 1

Data-to-Energy Stochastic Dynamics

arXiv:2509.26364v3 Announce Type: replace Abstract: The Schr\"odinger bridge problem is concerned with finding a stochastic dynamical system bridging two marginal distributions that minimises a certa...

By Kirill Tamogashev, Esmeralda S. Whitammer

diffusion reinforcement-learning multimodal

arXiv Machine Learning

Jun 2

Mean Flow Policy Optimization

arXiv:2604. 14698v2 Announce Type: replace Abstract: Diffusion models have recently emerged as expressive policy representations for online reinforcement learning (RL).

By Xiaoyi Dong, Xi Sheryl Zhang, Jian Cheng

diffusion reinforcement-learning benchmarks

One email a morning, machine-written

Related stories

Designing Reinforcement Learning for Diffusion Models: A Unified Path-Space View

Refinement-based Flow Policy Optimization

Amortizing intractable inference in diffusion models for vision, language, and control

Reinforcement Learning for Flow-Matching Policies with Density Transport

Data-to-Energy Stochastic Dynamics

Mean Flow Policy Optimization