LLMs for Engineering: Teaching Models to Design High Powered Rockets
Authors: name: Toby Simonds

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.