FoldQuantVLA: Native Low-Bit Quantization of Vision-Language-Action Models via Consistent Folding
Low-bit vision-language-action inference must reduce observation-to-action latency while preserving robot behavior. We present FoldQuantVLA, a post-training quantization framework that carries a consistent activation representation through calibration, weight rounding, and native integer execution. It combines channel scaling and block Hadamard transforms with dynamic per-token quantization, without policy retraining. Custom TensorRT plugins execute projections in both the language backbone and iterative action expert with four-bit weights and activations (W4A4) on Ada GPUs and Jetson AGX Orin. Evaluation spans LIBERO, SimplerEnv, and two robot platforms. Across three GR00T checkpoints an...
| Authors | Hung T. Ho, Khanh D. Nguyen, Quang D. Nguyen et al. |
|---|---|
| Primary category | cs.RO |
| Published | 21 September 2026 |
| Last updated at source | 21 September 2026 |
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