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
arXiv:2608.28878v1 Announce Type: cross
Abstract: This paper develops a hybrid offline-online multi-agent reinforcement learning framework based on decision transformers. The policy is first pretrain...
By Yiming Zhang, Kun Yang, Cong Shen, Dongning Guo
arXiv:2408. 05885v3 Announce Type: replace Abstract: Generative Flow Networks (GFlowNets) have been shown effective to generate combinatorial objects with desired properties.
By Puhua Niu, Shili Wu, Mingzhou Fan, Xiaoning Qian
arXiv:2607. 14272v1 Announce Type: new Abstract: Flow matching has emerged as an effective framework for learning complex data distributions, but adapting pretrained flow models to new tasks often requires computationally expensive retraining.
By Jingdong Zhang, Xinze Li, Yize Jiang, Luan Yang, Minkai Xu, Junhong Liu
arXiv:2605.01457v4 Announce Type: replace
Abstract: How can generative offline multi-agent reinforcement learning achieve both fast joint trajectory generation and effective cooperation? Multi-agent...
By Guowei Zou, Haitao Wang, Beiwen Zhang, Boning Zhang, Hejun Wu
DART-FL is a multitask federated learning framework designed for edge devices that must balance online inference and model training under limited resources. It dynamically allocates resources between inference and training based on current inference backlog and service capacity, then distributes remaining training capacity among tasks using a queue‑aware scheduler that adjusts loss weights. Experiments on image classification datasets with synthetic and real workloads show that DART‑FL adapts to bursty inference demand, improving accuracy for high‑demand tasks while preserving overall multitask performance.
By Yiming Xie, Pinrui Yu, Geng Yuan, Xue Lin, Ningfang Mi