JoyNexus: Service-Oriented Multi-Tenant Post-Training for VLA Models
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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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JoyNexus: Service-Oriented Multi-Tenant Post-Training for VLA Models
Public signals
Hugging Face upvotes 8 · Semantic Scholar citations 0 · Semantic Scholar influential citations 0
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.