arXiv AI
Jun 30

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.

By Qijun Li, Zheng Fu, Qi Song, Yifei He, Weitao Zhou, Kun Jiang, Diange Yang
arXiv Machine Learning
Jun 26

Reinforcement Fine-Tuning of Flow-Matching Policies for Vision-Language-Action Models

arXiv:2510. 09976v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models such as OpenVLA, Octo, and $\pi_0$ have shown strong generalization by leveraging large-scale demonstrations, yet their performance is still fundamentally constrained by the quality and coverage of supervised data.

By Mingyang Lyu, Yinqian Sun, Erliang Lin, Huangrui Li, Ruolin Chen, Feifei Zhao, Yi Zeng