arXiv AI June 16, 2026 By Ruisong Zhou, Haijun Zou, Li Zhou, Chumin Sun, Zaiwen Wen

A Learning Method with Gap-Aware Generation for Heterogeneous DAG Scheduling

arXiv:2603. 23249v2 Announce Type: replace-cross Abstract: Efficient scheduling of directed acyclic graphs (DAGs) is a core problem in large-scale data-intensive computing systems, where query plans, data-processing workloads, and computation graphs consist of dependent tasks competing for limited heterogeneous resource pools.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

reinforcement-learning

arXiv AI

Jul 14

Time-Lag-Aware Deep Reinforcement Learning for Flexible Job-Shop Scheduling in PPVC Module Factories

arXiv:2607. 11725v1 Announce Type: cross Abstract: Prefabricated prefinished volumetric construction moves most building work into module factories, whose production floor operates as a flexible job shop.

By Ziheng Zhang, Wei Zhang

rag reinforcement-learning benchmarks

arXiv AI

Jun 3

Libra: Efficient Resource Management for Agentic RL Post-Training

arXiv:2606. 03077v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a standard post-training paradigm for large language models (LLMs), extending beyond preference alignment to complex reasoning and multi-turn agentic behaviors.

By Kaiwen Chen, Xin Tan, Jingzong Li, Hong Xu

llms agents reinforcement-learning efficiency safety

arXiv AI

Jun 15

Scalable Production Scheduling: Linear Complexity via Unified Homogeneous Graphs

arXiv:2604. 23841v2 Announce Type: replace-cross Abstract: Efficiently solving the Job Shop Scheduling Problem in real-world industrial applications requires policies that are both computationally lean and topologically robust.

By Jonathan Hoss, Moritz Link, Noah Klarmann

agents diffusion reinforcement-learning benchmarks

arXiv AI

Jun 29

End-to-End Dynamic Sparsity for Resource-Adaptive LLM Inference

arXiv:2606. 27743v1 Announce Type: cross Abstract: Large Language Models (LLMs) inference is typically deployed under a static resource assumption, where models execute a fixed computational graph regardless of the runtime environment.

By Yuhang Chen, Jinhao Duan, Ruichen Zhang, Mingfu Liang, Xiaohan Wei, Yunchen Pu, Fei Tian, Chonglin Sun, Parish Aggarwal, Frank Shyu, Luke Simon, Sandeep Pandey, Tianlong Chen, Xi Liu

llms efficiency

arXiv Machine Learning

Jul 23

TempoNet: Slack-Quantized Transformer-Guided Reinforcement Scheduler for Adaptive Deadline-Centric Real-Time Dispatchs

arXiv:2602. 18109v3 Announce Type: replace Abstract: Real-time schedulers must reason about tight deadlines under strict compute budgets.

By Rong Fu, Yibo Meng, Zeyu Zhang, Ziming Guo, Jia Yee Tan, Xiaojing Du, Simon James Fong

llms rag nlp reinforcement-learning efficiency benchmarks

arXiv AI

Jun 2

Deft Scheduling of Dynamic Cloud Workflows with Varying Deadlines via Mixture-of-Experts

arXiv:2606. 01162v1 Announce Type: new Abstract: Workflow scheduling in cloud computing demands the intelligent allocation of dynamically arriving, graph-structured workflows with varying deadlines onto ever-changing virtual machine resources.

By Ya Shen, Gang Chen, Hui Ma, Mengjie Zhang

reinforcement-learning benchmarks

Related stories

Time-Lag-Aware Deep Reinforcement Learning for Flexible Job-Shop Scheduling in PPVC Module Factories

Libra: Efficient Resource Management for Agentic RL Post-Training

Scalable Production Scheduling: Linear Complexity via Unified Homogeneous Graphs

End-to-End Dynamic Sparsity for Resource-Adaptive LLM Inference

TempoNet: Slack-Quantized Transformer-Guided Reinforcement Scheduler for Adaptive Deadline-Centric Real-Time Dispatchs

Deft Scheduling of Dynamic Cloud Workflows with Varying Deadlines via Mixture-of-Experts