SwiftVLA: Unlocking Spatiotemporal Dynamics for Lightweight VLA Models at Minimal Overhead

Understand

Vision-Language-Action (VLA) models built on pretrained Vision-Language Models (VLMs) show strong potential but are limited in practicality due to their large parameter counts.

• To mitigate this issue, using a lightweight VLM has been explored, but it compromises spatiotemporal reasoning.
• Although some methods suggest that incorporating additional 3D inputs can help, they usually rely on large VLMs to fuse 3D and 2D inputs and still lack temporal understanding.
• Therefore, we propose SwiftVLA, an architecture that enhances a compact model with 4D understanding while preserving design efficiency.