Large Language Models (LLMs) have transformed software engineering, but their application to physical engineering domains remains underexplored. This paper evaluates LLMs' capabilities in high-powered rocketry design through RocketBench, a benchmark connecting LLMs to high-fidelity rocket simulations. We test models on two increasingly complex design tasks: target altitude optimization and precision landing challenges. Our findings reveal that while state-of-the-art LLMs demonstrate strong baseline engineering knowledge, they struggle to iterate on their designs when given simulation results and ultimately plateau below human performance levels. However, when enhanced with reinforcement learning (RL), we show that a 7B parameter model outperforms both SoTA foundation models and human experts. This research demonstrates that RL-trained LLMs can serve as effective tools for complex engineering optimization, potentially transforming engineering domains beyond software development.

本研究解决了大语言模型在物理工程领域应用不足的问题。通过RocketBench基准测试，我们展示了大语言模型在高功率火箭设计中的能力，发现经过强化学习增强的模型能够超越传统基础模型和人类专家。这表明强化学习训练的大语言模型能够有效支持复杂工程优化，可能在软件开发之外改变其他工程领域。

大语言模型在工程中的应用：教会模型设计高功率火箭