LLMs在工程領域的應用：教導模型設計高功率火箭

LLMs for Engineering: Teaching Models to Design High Powered Rockets

April 27, 2025

作者: Toby Simonds

cs.AI

摘要

大型語言模型（LLMs）已革新了軟體工程領域，但其在物理工程領域的應用仍待深入探索。本文通過RocketBench這一將LLMs與高保真火箭模擬相連的基準，評估了LLMs在高功率火箭設計中的能力。我們在兩個日益複雜的設計任務上測試了模型：目標高度優化和精準著陸挑戰。研究發現，儘管最先進的LLMs展現了紮實的基礎工程知識，但在獲得模擬結果後迭代設計時卻顯乏力，最終表現停滯於人類水平之下。然而，當結合強化學習（RL）進行增強後，我們展示了一個7B參數的模型超越了當前最強的基礎模型及人類專家。這項研究表明，經過RL訓練的LLMs能成為複雜工程優化的有效工具，有望在軟體開發之外的工程領域帶來變革。

English

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.