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JoyNexus: Service-Oriented Multi-Tenant Post-Training for VLA Models

Research Efficiency & Systems

Merged summary

TL;DR - JoyNexus is a multi-tenant service for fine-tuning, reinforcement learning, and evaluating Vision-Language-Action models. It reduces aggregate GPU time and improves utilization by sharing resident base models and coordinating workloads across tenants.

  • Separates training, inference, and environment services behind high- and low-level APIs.
  • Isolates tenant-specific modules, optimizers, rollout data, and policy versions while sharing infrastructure.
  • Uses global training and inference queues to schedule concurrent workloads.
  • Introduces group batching across compatible heterogeneous data schemas, sharing a backbone forward pass.

Sources (1)

JoyNexus: Service-Oriented Multi-Tenant Post-Training for VLA Models

arXiv cs.DC Haoran Sun, Wentao Zhang, Junyang Hua, Hedan Yang, Yongjian Guo, Yifei Zhang, Xiaolong Xiang, Mingxi Luo, Jing Long, Chen Zhao, Chen Zhou, Wanting Xu, Qiming Yang, Hui Zhang, Song Wang, Xiaodong Bai, Shuai Di, Xu Chu, Xiaotie Deng, Yicheng Gong, Junwu Xiong 2026-07-17 arXiv:2607.16074

TL;DR - JoyNexus is a multi-tenant service for fine-tuning, reinforcement learning, and evaluating Vision-Language-Action models. It reduces aggregate GPU time and improves utilization by sharing resident base models and coordinating workloads across tenants.

  • Separates training, inference, and environment services behind high- and low-level APIs.
  • Isolates tenant-specific modules, optimizers, rollout data, and policy versions while sharing infrastructure.
  • Uses global training and inference queues to schedule concurrent workloads.
  • Introduces group batching across compatible heterogeneous data schemas, sharing a backbone forward pass.
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