QuantVLA: Scale-Calibrated Post-Training Quantization for Vision-Language-Action Models
Vision-language-action (VLA) models unify perception, language, and control for embodied agents but face significant challenges in practical deployment due to rapidly increasing compute and memory demands, especially as models scale to longer horizons and larger backbones. To address these bottlenecks, we introduce QuantVLA, a training-free post-training quantization (PTQ) framework that, to our knowledge, is the first PTQ approach for VLA systems and the first to successfully quantize a diffusion transformer (DiT) action head. QuantVLA incorporates three scale-calibrated components: (1) a selective quantization layout that integerizes all linear layers in both the language backbone and t...
| Authors | Jingxuan Zhang, Yunta Hsieh, Zhongwei Wan et al. |
|---|---|
| Primary category | cs.LG |
| Published | 23 February 2026 |
| Last updated at source | 6 April 2026 |
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