Vision-Language-Action (VLA) models unify perception, reasoning, and control within a single policy, yet their multi-billion-parameter backbones and diffusion-based action heads make on-device deployment prohibitively expensive. Prior quantization efforts offer only partial solutions, compressing the LLM backbone while leaving the DiT action head at full precision, or resorting to mixed-precision schemes, driven by the belief that uniformly quantizing the action head is inherently unstable. We challenge this assumption with Omega-QVLA, the first training-free post-training quantization framework that compresses both the language backbone and the entire diffusion action head of a VLA model to a uniform W4A4 precision, eliminating the need for mixed-precision allocation. Omega-QVLA combines a composite SVD-Hadamard rotation that equalizes per-channel weight energy while diffusing residual activation outliers with per-step DiT activation scaling quantization that absorbs dynamic-range drift across denoising steps. On LIBERO, Omega-QVLA compresses Pi 0.5 and GR00T N1.5 to W4A4 with 98.0% and 87.8% task success rates, matching or exceeding their FP16 references of 97.1% and 87.0%, while reducing the static memory footprint by 71.3%. Real-world manipulation experiments further confirm smooth, accurate manipulation where prior methods fail. Code is available at https://github.com/UCMP13753/Omega-QVLA.

翻译：视觉-语言-动作模型将感知、推理与控制统一于单一策略中，但其数十亿参数的骨干网络与基于扩散的动作头使得设备端部署成本极其高昂。现有的量化方案仅提供部分解决方案——要么仅压缩大语言模型骨干网络而保留扩散Transformer动作头的全精度，要么采用混合精度方案，其根本原因在于认为对动作头进行统一量化存在固有不可靠性。本文提出的Omega-QVLA首次挑战这一假设，它是一种无需训练的后训练量化框架，能够将视觉-语言-动作模型的语言骨干网络与完整扩散动作头统一压缩至W4A4精度，从而消除混合精度分配的需求。Omega-QVLA创新性地融合了复合奇异值分解-哈达玛旋转（用于均衡逐通道权重能量并扩散残差激活异常值）与逐歩扩散Transformer激活缩放量化（用于吸收去噪步进间的动态范围偏移）。在LIBERO基准测试中，Omega-QVLA将Pi 0.5与GR00T N1.5模型压缩至W4A4精度后，任务成功率分别达到98.0%与87.8%，匹配甚至超越其FP16参考模型的97.1%与87.0%，同时静态内存占用减少71.3%。真实世界的机械臂操作实验进一步验证了该框架在先前方法失效场景下仍能实现流畅精准的操作。代码已开源至https://github.com/UCMP13753/Omega-QVLA。