ActQuant: Sub-4-bit Action-Guided Quantization for Vision-Language-Action Models

• URL: http://arxiv.org/abs/2605.24011v1
• Date: Tue, 19 May 2026 19:57:26 GMT
• Title: ActQuant: Sub-4-bit Action-Guided Quantization for Vision-Language-Action Models
• Abstract summary: We introduce ActQuant, an action-guided mixed-precision PTQ framework.<n>ActQuant operates in two stages: (1) an inter-tensor bit allocator that assigns each weight matrix a single bitwidth based on how much it contributes to predicting the agent's actions.<n>We also introduce OmniModel, an agentic conversion pipeline that ports into a native C/C++ with efficient low-bit kernels.
• Score: 45.62029693245481
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Vision-Language-Action (VLA) models exhibit remarkable action generation for embodied intelligence, but their heavy compute make deployment on edge platforms impractical. Aggressive, sub-4-bit weight quantization is the natural solution, yet existing post-training quantization (PTQ) methods suffer severe performance degradation in this regime. To address this, we introduce ActQuant, an action-guided mixed-precision PTQ framework that operates in two stages: (1) an inter-tensor bit allocator that assigns each weight matrix a single bit-width based on how much it contributes to predicting the agent's actions; (2) an intra-tensor scale optimizer tunes per-block quantization scales using action-aware curvature, so that dynamic range is concentrated on the weights most influential for control. To deliver the on-device benefits of our aggressive quantization, we further introduce OmniModel.cpp, an agentic conversion pipeline that ports architectures into a native C/C++ runtime with efficient low-bit kernels. We evaluate ActQuant both in simulation and on a real-world 6-DoF UR3 arm, with all models deployed through OmniModel.cpp. On the LIBERO benchmark, ActQuant is the only method that operates at or below 3 bits-per-weight, retaining 95.0% on OpenVLA-OFT and 94.8% on $π_{0.5}$. Pushed further, ActQuant reaches 2.5 bpw at 90.1% on OpenVLA-OFT, compressing the backbone from 14.3 GB to 2.7 GB (5.3$\times$). On the physical UR3 arm, $π_{0.5}$ quantized with ActQuant retains the baseline's success rate while reducing the memory footprint by 2.5$\times$.

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