title: LLMs for Engineering: Teaching Models to Design High Powered Rockets
summary: 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.
paper_authors._id: 6814b0279488d28ae012b071
paper_authors.name: Toby Simonds
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-05T07:46:43.344Z
paper_authors._id: 649e1f4d2ccae3ea1f2b6a7f
paper_authors.avatarUrl: /avatars/04657fb53bde660dafb743da4273325f.svg
paper_authors.fullname: Toby Simonds
paper_authors.type: user
paper_authors.user: TamasSimonds
paper_title: LLMs for Engineering: Teaching Models to Design High Powered Rockets
paper_summary: 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.
paper_ai_summary: Reinforcement learning enhances large language models to outperform humans in complex rocket design optimization tasks, suggesting broader applications in engineering.
paper_ai_keywords: Large Language Models RocketBench high-fidelity rocket simulations target altitude optimization precision landing challenges reinforcement learning SoTA foundation models