arXiv AI June 30, 2026 By Qijun Li, Zheng Fu, Qi Song, Yifei He, Weitao Zhou, Kun Jiang, Diange Yang

Dual-Flow Reinforcement Learning with State-Aware Exploration

arXiv:2606. 29820v1 Announce Type: cross Abstract: In complex continuous-control reinforcement learning tasks, multimodal optimal actions often coincide with uncertain, multimodal return distributions, making reliable value estimation and multimodal exploration challenging.

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 AI.

arXiv AI

Sep 15

Learning Multimodal One-step Flow Policy via Value-weighted Optimal Transport

arXiv:2609.15883v1 Announce Type: cross Abstract: Offline reinforcement learning aims to learn a policy solely from fixed datasets, which often contain multimodal action distributions. Flow policies...

By Jaehun Shon, Jinha Choi, Jongwook Jeon, Jongmin Lee

reinforcement-learning efficiency multimodal benchmarks safety

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

Sep 15

VGFM: Expressive Robot Policies via Dense Value Guidance in Flow Matching

arXiv:2609.14261v1 Announce Type: cross Abstract: Recent robot learning paradigms increasingly rely on large offline datasets of robotic interactions to train control policies. Expressive generative...

By Prajwal Koirala, Mark Campbell

diffusion reinforcement-learning robotics efficiency multimodal

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

Jun 8

GenPO++: Generative Policy Optimization with Jacobian-free Likelihood Ratios

arXiv:2606. 06967v1 Announce Type: new Abstract: Generative policies provide expressive and multimodal action distributions, making them attractive for reinforcement learning (RL) in complex continuous-control tasks.

By Ke Hu, Shutong Ding, Panxin Tao, Jingya Wang, Ye Shi

reinforcement-learning robotics fine-tuning multimodal benchmarks safety

arXiv AI

Jun 24

FlowR2A: Learning Reward-to-Action Distribution for Multimodal Driving Planning

arXiv:2606. 24231v1 Announce Type: new Abstract: Multimodal driving planning faces a long-standing tension between two paradigms: scoring-based methods benefit from dense reward supervision but are confined to a fixed action vocabulary, while anchor-based methods generate proposals dynamically yet suffer from sparse supervision constrained to a single ground-truth trajectory.

By Xirui Li, Zhe Liu, Xiaoqing Ye, Wenhua Han, Yifeng Pan, Junyu Han, Hengshuang Zhao

multimodal benchmarks safety

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Related stories

Learning Multimodal One-step Flow Policy via Value-weighted Optimal Transport

Refinement-based Flow Policy Optimization

VGFM: Expressive Robot Policies via Dense Value Guidance in Flow Matching

Reinforcement Learning for Flow-Matching Policies with Density Transport

GenPO++: Generative Policy Optimization with Jacobian-free Likelihood Ratios

FlowR2A: Learning Reward-to-Action Distribution for Multimodal Driving Planning