Imagen de Portada

Polar: Agentic RL on Any Harness at Scale

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Xu, Binfeng, Zhang, Hao, Zhang, Shaokun, Han, Songyang, Liu, Mingjie, Hu, Jian, Diao, Shizhe, Jin, Zhenghui, Zou, Yunheng, Demoret, Michael, Kautz, Jan, Dong, Yi
Formato:	Preprint
Publicado:	2026
Materias:	Distributed, Parallel, and Cluster Computing
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

ROSE: Rollout On Serving GPUs via Cooperative Elasticity for Agentic RL
por: Gao, Wei, et al.
Publicado: (2026)

KIS-S: A GPU-Aware Kubernetes Inference Simulator with RL-Based Auto-Scaling
por: Zhang, Guilin, et al.
Publicado: (2025)

vLLM-Omni: Fully Disaggregated Serving for Any-to-Any Multimodal Models
por: Yin, Peiqi, et al.
Publicado: (2026)

AgentServe: Algorithm-System Co-Design for Efficient Agentic AI Serving on a Consumer-Grade GPU
por: Zhang, Yuning, et al.
Publicado: (2026)

DualPath: Breaking the Storage Bandwidth Bottleneck in Agentic LLM Inference
por: Wu, Yongtong, et al.
Publicado: (2026)

Efficient Multi-Worker Selection based Distributed Swarm Learning via Analog Aggregation
por: Yao, Zhuoyu, et al.
Publicado: (2025)

HetRL: Efficient Reinforcement Learning for LLMs in Heterogeneous Environments
por: He, Yongjun, et al.
Publicado: (2025)

LLM-assisted Agentic Edge Intelligence Framework
por: Dehury, Chinmaya Kumar, et al.
Publicado: (2026)

FlashFuser: Expanding the Scale of Kernel Fusion for Compute-Intensive Operators via Inter-Core Connection
por: Huang, Ziyu, et al.
Publicado: (2025)

Schedule-Level Shared-Prefix Reuse for LLM RL Training
por: Li, Pengbo, et al.
Publicado: (2026)

AME: An Efficient Heterogeneous Agentic Memory Engine for Smartphones
por: Zhao, Xinkui, et al.
Publicado: (2025)

RollArt: Scaling Agentic RL Training via Disaggregated Infrastructure
por: Gao, Wei, et al.
Publicado: (2025)

HiRL: Hierarchical Reinforcement Learning for Coordinated Resource Management in Heterogeneous Edge Computing
por: Zhu, Jianyong, et al.
Publicado: (2026)

DistFlow: A Fully Distributed RL Framework for Scalable and Efficient LLM Post-Training
por: Wang, Zhixin, et al.
Publicado: (2025)

HAP: SPMD DNN Training on Heterogeneous GPU Clusters with Automated Program Synthesis
por: Zhang, Shiwei, et al.
Publicado: (2024)

On the Decidability of Distributed Tasks with Output Sets under Asynchrony and Any Number of Crashes
por: Albouy, Timothé, et al.
Publicado: (2026)

TokenDance: Scaling Multi-Agent LLM Serving via Collective KV Cache Sharing
por: Bian, Zhuohang, et al.
Publicado: (2026)

MegaFlow: Large-Scale Distributed Orchestration System for the Agentic Era
por: Zhang, Lei, et al.
Publicado: (2026)

Distributed Generative Inference of LLM at Internet Scales with Multi-Dimensional Communication Optimization
por: Chen, Jiu, et al.
Publicado: (2026)

Agentic AI Workload Characteristics
por: Yuan, Yichao, et al.
Publicado: (2026)

PolarStore: High-Performance Data Compression for Large-Scale Cloud-Native Databases
por: Hu, Qingda, et al.
Publicado: (2025)

Cephalo: Harnessing Heterogeneous GPU Clusters for Training Transformer Models
por: Guo, Runsheng Benson, et al.
Publicado: (2024)

Benchmarking the Performance of Large Language Models on the Cerebras Wafer Scale Engine
por: Zhang, Zuoning, et al.
Publicado: (2024)

A Spark Optimizer for Adaptive, Fine-Grained Parameter Tuning
por: Lyu, Chenghao, et al.
Publicado: (2024)

Ventilator pressure prediction using recurrent neural network
por: Diao, Su, et al.
Publicado: (2024)

Agentic AI-Driven UAV Network Deployment: An LLM-Enhanced Exact Potential Game Approach
por: Tang, Xin, et al.
Publicado: (2026)

StreamRL: Scalable, Heterogeneous, and Elastic RL for LLMs with Disaggregated Stream Generation
por: Zhong, Yinmin, et al.
Publicado: (2025)

CONCUR: High-Throughput Agentic Batch Inference of LLM via Congestion-Based Concurrency Control
por: Chen, Qiaoling, et al.
Publicado: (2026)

A New Perspective of Graph Data and A Generic and Efficient Method for Large Scale Graph Data Traversal
por: Zhang, Chenglong
Publicado: (2020)

RollMux: Phase-Level Multiplexing for Disaggregated RL Post-Training
por: Wu, Tianyuan, et al.
Publicado: (2025)

Cornfigurator: Automated Planning for Any-to-Any Multimodal Model Serving
por: Ma, Jeff J., et al.
Publicado: (2025)

HarMoEny: Efficient Multi-GPU Inference of MoE Models
por: Doucet, Zachary, et al.
Publicado: (2025)

PROSERVE: Unified Multi-Priority Request Scheduling for LLM Serving
por: Huang, Weizhe, et al.
Publicado: (2025)

Optimizing FaaS Platforms for MCP-enabled Agentic Workflows
por: Kulkarni, Varad, et al.
Publicado: (2026)

ElasticMoE: An Efficient Auto Scaling Method for Mixture-of-Experts Models
por: Singh, Gursimran, et al.
Publicado: (2025)

Bandwidth-Aware LLM Inference on Heterogeneous Many-Core Supercomputers
por: Lu, Yao, et al.
Publicado: (2026)

RL over Commodity Networks: Overcoming the Bandwidth Barrier with Lossless Sparse Deltas
por: Ruan, Chaoyi, et al.
Publicado: (2026)

Matrix-PIC: Harnessing Matrix Outer-product for High-Performance Particle-in-Cell Simulations
por: Rao, Yizhuo, et al.
Publicado: (2026)

ExpertWeave: Efficiently Serving Expert-Specialized Fine-Tuned Adapters at Scale
por: Shi, Ge, et al.
Publicado: (2025)

Cornserve: A Distributed Serving System for Any-to-Any Multimodal Models
por: Chung, Jae-Won, et al.
Publicado: (2026)