Vlaser: Vision-Language-Action Model with Synergistic Embodied Reasoning
Authors: name: Ganlin Yang, name: Tianyi Zhang, name: Haoran Hao, name: Weiyun Wang, name: Yibin Liu, name: Dehui Wang, name: Guanzhou Chen, name: Zijian Cai, name: Junting Chen, name: Weijie Su, name: Wengang Zhou, name: Yu Qiao, name: Jifeng Dai, name: Jiangmiao Pang, name: Gen Luo, name: Wenhai Wang, name: Yao Mu, name: Zhi Hou
Organization: shanghai ailab 

While significant research has focused on developing embodied reasoning
capabilities using Vision-Language Models (VLMs) or integrating advanced VLMs
into Vision-Language-Action (VLA) models for end-to-end robot control, few
studies directly address the critical gap between upstream VLM-based reasoning
and downstream VLA policy learning. In this work, we take an initial step
toward bridging embodied reasoning with VLA policy learning by introducing
Vlaser - a Vision-Language-Action Model with synergistic embodied reasoning
capability, which is a foundational vision-language model designed to integrate
high-level reasoning with low-level control for embodied agents. Built upon the
high-quality Vlaser-6M dataset, Vlaser achieves state-of-the-art performance
across a range of embodied reasoning benchmarks - including spatial reasoning,
embodied grounding, embodied QA, and task planning. Furthermore, we
systematically examine how different VLM initializations affect supervised VLA
fine-tuning, offering novel insights into mitigating the domain shift between
internet-scale pre-training data and embodied-specific policy learning data.
Based on these insights, our approach achieves state-of-the-art results on the
WidowX benchmark and competitive performance on the Google Robot benchmark.