4 accepted papers
We study a new online matching problem termed Online Capacitated General Matching with Knapsack (OCGMK), which generalizes the Online General Matching (OGM) problem. In the original OGM, vertices arrive sequentially and need to be paired with other vertices to maximize the total reward of pairing. O
We study the online Generalized Magician’s Problem with Multiple Workers (GMPMW), where tasks arrive sequentially and must be assigned to one of several workers for processing, with each worker consuming a stochastic amount of resources and generating an unknown reward. The system must decide on the
Long-CoT reasoning combined with reinforcement learning for large language models demonstrates remarkable performance and scalability. However, we observe that the initial policy model could significantly influence the final performance as well as the token efficiency. Additionally, there is a lack…
We study a new online assignment problem, called the Online Task Assignment with Controllable Processing Time. In a bipartite graph, a set of online vertices (tasks) should be assigned to a set of offline vertices (machines) under the known adversarial distribution (KAD) assumption. We are the firs…