Ω-QVLA is a training-free post-training quantization (PTQ) framework designed to compress both the language backbone and the entire diffusion action head of Vision-Language-Action (VLA) models to a uniform W4A4 precision.
This specific checkpoint is based on the GR00T N1.5 architecture. Ω-QVLA enables the deployment of large VLA models on-device by significantly reducing their memory footprint while maintaining performance.
Key Features
Uniform W4A4 Quantization: The first framework to compress both the LLM backbone and the diffusion action head to 4-bit precision without needing mixed-precision allocation.
Composite SVD-Hadamard Rotation: Equalizes weight energy and diffuses activation outliers.
Per-step DiT Activation Scaling: Absorbs dynamic-range drift across denoising steps in the action head.
Efficiency: Reduces the static memory footprint by approximately 71.3% while matching or exceeding FP16 success rates on benchmarks like LIBERO.
