GEM: Generative Supervision Helps Embodied Intelligence

Embodied Vision-Language Models (VLMs) have demon-strated impressive performance and generalization in robotics, partic-ularly within Vision-Language-Action frameworks. However, a signifi-cant gap remains between the high-level semantic focus of standardtext-guided pre-training paradigms and the low-level spatial and phys-ical knowledge critical for execution in embodied environments. In thispaper, we introduce GEM, a Generative-supervised Embodied vision-language Model designed to bridge this divide. We propose integratinga depth map generation task directly into the VLM pre-training phase.By training this generative objective jointly with the main model, weobserve substantial improvements in embodied intelligence, significantlyenhancing both semantic understanding and physical operation capa-bilities. To support this paradigm, we curate and release GEM-4M, acomprehensive large-scale dataset featuring a mixture of grounding, rea-soning, and planning data paired with high-quality depth supervision.Extensive experiments demonstrate that GEM achieves state-of-the-artresults across diverse embodied benchmarks. Furthermore, our deployedaction model, GEM-VLA, exhibits vastly superior task execution abilitiesin both simulation environments and real-world evaluations. Code, mod-els, and datasets are available at https://zhaorw02.github.io/GEM/.