arXiv:2607. 28925v1 Announce Type: new Abstract: Dynamic matching markets require decisions about whom to match and when: matching now yields value but removes participants who may create better future opportunities.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2606. 06744v1 Announce Type: new Abstract: Two-sided matching markets often involve information that unfolds over time through interviews, repeated interaction, learning, and separation.
arXiv:2608. 09130v1 Announce Type: cross Abstract: Allocating limited computation among concurrent learning tasks is difficult when each task must reach a target loss before a deadline but its required training effort is unknown.
arXiv:2606. 00367v1 Announce Type: cross Abstract: Reinforcement learning problems typically define the goal as maximizing the expected value of a scalar reward function.
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.
arXiv:2606. 27780v1 Announce Type: new Abstract: World models are often used for planning by rolling learned dynamics forward.
arXiv:2606. 06353v1 Announce Type: new Abstract: Machine learning is increasingly employed for the evaluation of football tactics.