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Computer Science > Software Engineering

arXiv:2504.19394 (cs)
[Submitted on 27 Apr 2025 (v1), last revised 29 Apr 2025 (this
version, v2)]

Title:LLMs for Engineering: Teaching Models to Design High Powered
Rockets

Authors:Toby Simonds
View a PDF of the paper titled LLMs for Engineering: Teaching Models
to Design High Powered Rockets, by Toby Simonds
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    Abstract: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.

Subjects: Software Engineering (cs.SE); Artificial Intelligence
          (cs.AI)
Cite as:  arXiv:2504.19394 [cs.SE]
          (or arXiv:2504.19394v2 [cs.SE] for this version)
          https://doi.org/10.48550/arXiv.2504.19394
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Toby Simonds [view email]
[v1] Sun, 27 Apr 2025 23:59:39 UTC (638 KB)
[v2] Tue, 29 Apr 2025 22:15:42 UTC (638 KB)
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