Abstract
Embodied decision-making enables agents to translate high-level goals into executable actions through continuous interactions within the physical world, forming a cornerstone of general-purpose embodied intelligence. Large language models (LLMs), with their general decision-making capabilities, offer a promising path to realize this potential; however, LLMs trained solely on language lack exposure to physical environments, limiting their true embodied understanding. To bridge this gap, we propose the concept of a training ground: a comprehensive infrastructure that provides task and scene simulation, embodied interaction, and feedback signals, offering a one-stop solution for LLM acquire genuine embodied decision-making skills. In this work, we present EmboMatrix, the first training ground of its kind, providing massive and diverse tasks with efficient simulation and precise rewards. EmboMatrix incorporates a series of novel techniques: a multi-agent data engine for large-scale task and scene generation, a distributed heterogeneous-hardware system for scalable simulation, and a multi-level reward architecture for precise supervision. Leveraging EmboMatrix, we cultivate EmboBrain, an LLM whose embodied decision-making abilities emerge from extensive embodied interactions. Experiments show that EmboBrain-7B surpasses the 671B DeepSeek-R1 baseline by 9.5\% on two challenging embodied decision-making benchmarks, demonstrating the power of interactive, environment-grounded learning for building truly intelligent embodied agents.
Abstract (translated)
嵌入式决策制定使代理能够通过与物理世界的持续互动将高层次的目标转化为可执行的动作，这是通用型嵌入智能的核心。大型语言模型（LLM）凭借其广泛的应用决策能力为实现这一潜力提供了可能路径；然而，仅接受语言训练的LLM缺乏对物理环境的实际接触，这限制了它们在真正意义上理解并适应物理世界的能力。为了弥合这种差距，我们提出了一种训练平台的概念：一个提供任务和场景模拟、嵌入式互动及反馈信号的全面基础设施，为LLM获取真正的嵌入式决策制定技能提供一站式解决方案。 在这项工作中，我们介绍了EmboMatrix——首个此类训练平台，它提供了大量且多样化的任务，并配备了高效仿真的功能和精确奖励机制。EmboMatrix整合了一系列新颖技术：一个多代理数据引擎用于大规模的任务和场景生成、一个分布式的异构硬件系统支持可扩展模拟以及一个多层级的奖励架构以实现精准监督。 通过利用EmboMatrix，我们培养出了EmboBrain——一种LLM，其嵌入式决策能力源于广泛的物理世界互动。实验表明，在两个具有挑战性的嵌入式决策制定基准测试中，EmboBrain-7B的表现比671B的DeepSeek-R1基线模型高出9.5%，这证明了基于环境交互的学习对于构建真正智能的嵌入式代理的重要性。
URL
https://arxiv.org/abs/2510.12072
