:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Zhu, Siqi, You, Jiaxuan
Format:	Preprint
Published:	2026
Subjects:	Artificial Intelligence Distributed, Parallel, and Cluster Computing
Online Access:	https://arxiv.org/abs/2601.07376
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Cores
by: Mei, Zhiyu, et al.
Published: (2023)

ARL-Tangram: Unleash the Resource Efficiency in Agentic Reinforcement Learning
by: Xiao, Bangjun, et al.
Published: (2026)

Scepsy: Serving Agentic Workflows Using Aggregate LLM Pipelines
by: Wagenländer, Marcel, et al.
Published: (2026)

Mesh-Attention: A New Communication-Efficient Distributed Attention with Improved Data Locality
by: Chen, Sirui, et al.
Published: (2025)

LogAct: Enabling Agentic Reliability via Shared Logs
by: Balakrishnan, Mahesh, et al.
Published: (2026)

MARLaaS: Multi-Tenant Asynchronous Reinforcement Learning as a Service
by: Yu, Timothy Tin Long, et al.
Published: (2026)

Trade-offs in Decentralized Agentic AI Discovery Across the Compute Continuum
by: Dazzi, Patrizio, et al.
Published: (2026)

KAIROS: Stateful, Context-Aware Power-Efficient Agentic Inference Serving
by: Yuan, Yichao, et al.
Published: (2026)

Safactory: A Scalable Agentic Infrastructure for Training Trustworthy Autonomous Intelligence
by: Chen, Xinquan, et al.
Published: (2026)

Towards using Reinforcement Learning for Scaling and Data Replication in Cloud Systems
by: Mokadem, Riad, et al.
Published: (2024)

The (R)evolution of Scientific Workflows in the Agentic AI Era: Towards Autonomous Science
by: Shin, Woong, et al.
Published: (2025)

Deep Reinforcement Learning for Fault-Adaptive Routing in Eisenstein-Jacobi Interconnection Topologies
by: Charrwi, Mohammad Walid, et al.
Published: (2026)

Reinforcement Learning-driven Data-intensive Workflow Scheduling for Volunteer Edge-Cloud
by: Mounesan, Motahare, et al.
Published: (2024)

Quantifying Energy and Cost Benefits of Hybrid Edge Cloud: Analysis of Traditional and Agentic Workloads
by: Alamouti, Siavash
Published: (2025)

Cooperative Cognitive Dynamic System in UAV Swarms: Reconfigurable Mechanism and Framework
by: Jia, Ziye, et al.
Published: (2024)

iScheduler: Reinforcement Learning-Driven Continual Optimization for Large-Scale Resource Investment Problems
by: Hu, Yi-Xiang, et al.
Published: (2026)

Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Review
by: Gu, Yan, et al.
Published: (2025)

ParaGAN: A Scalable Distributed Training Framework for Generative Adversarial Networks
by: Shi, Ziji, et al.
Published: (2024)

DeF-DReL: Systematic Deployment of Serverless Functions in Fog and Cloud environments using Deep Reinforcement Learning
by: Dehury, Chinmaya Kumar, et al.
Published: (2021)

Verify Distributed Deep Learning Model Implementation Refinement with Iterative Relation Inference
by: Wang, Zhanghan, et al.
Published: (2025)

High-Performance Parallel Optimization of the Fish School Behaviour on the Setonix Platform Using OpenMP
by: Wang, Haitian, et al.
Published: (2025)

PRAGMA: A Profiling-Reasoned Multi-Agent Framework for Automatic Kernel Optimization
by: Lei, Kelun, et al.
Published: (2025)

An Advanced Reinforcement Learning Framework for Online Scheduling of Deferrable Workloads in Cloud Computing
by: Dong, Hang, et al.
Published: (2024)

Delta Sum Learning: an approach for fast and global convergence in Gossip Learning
by: Goethals, Tom, et al.
Published: (2025)

Leyline: KV Cache Directives for Agentic Inference
by: Ma, Bole, et al.
Published: (2026)

Pact: A Choreographic Language for Agentic Ecosystems
by: Gopinathan, Kiran, et al.
Published: (2026)

Efficient and Scalable Agentic AI with Heterogeneous Systems
by: Asgar, Zain, et al.
Published: (2025)

High-Dimensional Data Processing: Benchmarking Machine Learning and Deep Learning Architectures in Local and Distributed Environments
by: Rodriguez, Julian, et al.
Published: (2025)

Janus: Collaborative Vision Transformer Under Dynamic Network Environment
by: Jiang, Linyi, et al.
Published: (2025)

APWA: A Distributed Architecture for Parallelizable Agentic Workflows
by: Rose, Evan, et al.
Published: (2026)

Boosting Asynchronous Decentralized Learning with Model Fragmentation
by: Biswas, Sayan, et al.
Published: (2024)

OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models
by: Xue, Fuzhao, et al.
Published: (2024)

Domain-Adaptive Model Merging Across Disconnected Modes
by: Liu, Junming, et al.
Published: (2026)

The intelligent prediction and assessment of financial information risk in the cloud computing model
by: Wang, Yufu, et al.
Published: (2024)

Loss- and Reward-Weighting for Efficient Distributed Reinforcement Learning
by: Holen, Martin, et al.
Published: (2023)

Deep Reinforcement Learning for System-on-Chip: Myths and Realities
by: Sung, Tegg Taekyong, et al.
Published: (2022)

Interpretable Modeling of Deep Reinforcement Learning Driven Scheduling
by: Li, Boyang, et al.
Published: (2024)

Tesserae: Scalable Placement Policies for Deep Learning Workloads
by: Bian, Song, et al.
Published: (2025)

Collaborative Split Federated Learning with Parallel Training and Aggregation
by: Papageorgiou, Yiannis, et al.
Published: (2025)

Sentinel: An Aggregation Function to Secure Decentralized Federated Learning
by: Feng, Chao, et al.
Published: (2023)