JoyNexus：面向VLA模型的多租户后训练服务
JoyNexus: Service-Oriented Multi-Tenant Post-Training for VLA Models
做VLA模型微调或机器人仿真的团队可以看看，JoyNexus用分组批处理省GPU资源，多租户场景下效率更高。
JoyNexus是一个统一服务，支持多租户VLA模型的监督微调、强化学习和评估。它通过解耦Training Model Service、Inference Model Service和Environment Service，租户通过API调用。
采用group batching技术，使不同VLA数据模式共享一个前向传播，提升训练效率。实验表明，相比单租户独立执行，JoyNexus减少了总GPU时间并提高了服务利用率。
JoyNexus: Service-Oriented Multi-Tenant Post-Training for VLA Models
The post-training of Vision-Language-Action (VLA) models is essential due to the diversity of simulators, robot embodiments, and task objectives. Existing compute services, whether offered as direct accelerator rental or batch-workload submission, typically allocate an exclusive set of GPU and CPU resources to a single tenant. While this paradigm maximizes client flexibility, it burdens users with infrastructure adaptation, and the fixed card-hour accounting model renders short or bursty workloads both expensive for tenants and inefficient for the service provider. To address these challenges, we present JoyNexus, a unified service for multi-tenant VLA supervised fine-tuning, reinforcement learning, and evaluation. JoyNexus decouples the Training Model Service, Inference Model Service, and Environment Service, each accessed through APIs and backed by resident shared base models with tenant-specific slots. Tenants can directly invoke high-level semantic APIs for training, rollout, and evaluation, or compose custom algorithms using lower-level APIs and their assigned endpoints. Multiple tenants submit workloads concurrently; their action modules, optimizers, rollout records, and policy versions remain isolated, and the service is scheduled by the global Training Queue and Inference Queue. To further improve multi-tenant training efficiency, JoyNexus introduces group batching for heterogeneous VLA data schemas that share a compatible model-facing prefix, enabling a single shared backbone forward pass over grouped samples. Finally, we evaluate JoyNexus through workload simulation and a group-batching pipeline in a realistic embodied scenario. Results show that, compared with isolated single-tenant execution, JoyNexus reduces aggregate GPU time and improves service utilization via cross-tenant scheduling on shared resources.
This report is categorized under AITOP's "Papers" desk. Key entities and technical topics under continuous tracking include JoyNexus, VLA, multi-tenancy, post-training.
Source attribution: curated from arXiv cs.AI (@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).
