行動誘導型超低ビット量子化：VLAモデルのオンボード展開に向けて
AGQ: Action-Guided Ultra-Low-Bit Quantization for Vision-Language-Action Models
視覚言語行動モデルの重みを、行動感度と復元コストを指標に行列ごとにビット幅配分して超低ビット量子化し、LIBEROで高成功率を保ちつつ大幅な圧縮を実現した研究。
著者: Xuhong Qiu, Zhuoyu Wu, Qiawei Zheng, Wenhui Ou, Wenqi Fang, Raphaël C. -W. Phan
分類: cs.CV, cs.AI
原文アブストラクト
Vision-language-action (VLA) models enable robots to perform tasks specified through natural-language instructions, but their large weight footprint challenges deployment on memory-constrained onboard platforms. Ultra-low-bit quantization reduces this footprint, yet preserving task performance and controlling weight recovery costs remain challenging. We propose Action-Guided Quantization, which combines action sensitivity with a measured proxy for weight recovery cost to allocate matrix bitwidths under storage budgets. Per-channel calibration compensates for quantization error, while a uniform bitwidth within each matrix preserves regular layouts. Bit-dependent grouped lossless coding reduces resident weight storage, and block-wise recovery limits temporary buffers. On LIBERO, AGQ achieves 92.7% task success at W3, exceeding AWQ and GPTQ by 1.2 and 3.7 percentage points, respectively, and retains 70.5% success at W2.5. An asymmetric configuration, denoted W1.5$^{*}$, uses W4 vision weights and a parameter-weighted average of 1.5 bits for language weights. With lossless coding, it achieves $6.10\times$ complete-model compression relative to BF16 while retaining 25.5% task success. Code and experimental results are available at https://github.com/Qiuxu00/AGQ