W EAVE : Efficient Co-Scheduling for Disaggregated RL Post-Training

Dynamic Compute and Network Orchestration for Disaggregated RL

This work builds Silverstone to orchestrate dynamically both compute and network in disaggregated RL, using a reconfigurable optical-electrical fabric called RFabric that achieves superior performance-cost efficiency at scale over static Fat-Tree networks.

HRL-TaskOpt: A Hierarchical Reinforcement Learning-Based Task Scheduling Framework for Multi-Cloud and Hybrid Environments

HRL-TaskOpt is presented, a novel Hierarchical Reinforcement Learning-based task scheduling framework that combines high-level global task offloading with millisecond-granularity local scheduling policies in an edge–cloud scenario and offers a scalable and intelligent solution for adaptive task scheduling.

psRL: Efficient Training for Agentic AI via Training-Time Prefix Sharing

This paper proposes psRL (prefix sharing for RL), a new training system for agentic AI designed to exploit prefix redundancy among training samples, and introduces two novel prefix-sharing mechanisms that enable flexible, fine-grained workload distribution across GPU workers.

Scheduling for Green Computility via LLM-Guided Deep Reinforcement Learning

Green Computility scheduling in heterogeneous clusters requires balancing execution efficiency, deadline satisfaction, resource utilization, carbon emissions, and renewable energy usage under dynamic workload and energy conditions. Existing heuristic schedulers are efficient but often rely on fixed hand crafted rules,...

Learning Workflow Scheduling on Multi-Resource Clusters

Deep Reinforcement Learning-based Dynamic TWT Scheduling for Heterogeneous Wi-Fi Networks

Target Wake Time (TWT) in IEEE 802.11ax enables significant power savings by coordinating station sleep schedules, but optimal TWT parameter selection under heterogeneous traffic remains an open challenge. We present a deep reinforcement learning framework for dynamic TWT scheduling that is protocol-compliant by design...