D-VLA: Distributed RL Framework for Embodied AI Models
Researchers propose D-VLA, a high-concurrency distributed reinforcement learning framework for Vision-Language-Action (VLA) models in Embodied AI. The framework addresses systemic bottlenecks from resource conflicts between high-fidelity physical simulation and deep learning's VRAM/bandwidth demands. D-VLA introduces 'Plane Decoupling' to physically isolate high-frequency training data from low-frequency weight control, eliminating interference between simulation and optimization. A four-thread asynchronous 'Swimlane' pipeline enables full parallel overlap of sampling and training. The work is detailed in arXiv preprint 2605.13276.
Key facts
- D-VLA is a distributed RL framework for VLA models
- Addresses bottlenecks from simulation and deep learning resource conflicts
- Introduces Plane Decoupling to isolate training data and weight control
- Uses a four-thread asynchronous Swimlane pipeline
- Published as arXiv:2605.13276
- Focuses on large-scale embodied foundation models
- Aims for high concurrency and low latency
- Targets Embodied AI applications
Entities
Institutions
- arXiv