# Autonomous Reasoning for Spacecraft Control: A Large Language Model Framework with Group Relative Policy Optimization

arXiv: https://arxiv.org/abs/2601.04334 · HTML: https://mlsanity.com/paper/2601.04334/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Reinforcement learning for reasoning, one of 17 directions in LLM reasoning.
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 7 Jan 2026.

## Placed in

- LLM reasoning / [Reinforcement learning for reasoning](https://mlsanity.com/lr/reinforcement-learning-for-reasoning/)

## Builds on (most cited first, 9 of 9)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Fine-Tuned Language Models as Space Systems Controllers](https://mlsanity.com/paper/2501.16588/) (2025)
- [Multi-Phase Spacecraft Trajectory Optimization via Transformer-Based Reinforcement Learning](https://mlsanity.com/paper/2511.11402/) (2025)
- [Tiny Recursive Control: Iterative Reasoning for Efficient Optimal Control](https://mlsanity.com/paper/2512.16824/) (2025)
- [Fine-tuning LLMs for Autonomous Spacecraft Control: A Case Study Using Kerbal Space Program](https://mlsanity.com/paper/2408.08676/) (2024)
- [A Survey on Integration of Large Language Models with Intelligent Robots](https://mlsanity.com/paper/2404.09228/) (2024)

## Built on by (most cited first, 4 of 4)

- [Intent-aligned Autonomous Spacecraft Guidance via Reasoning Models](https://mlsanity.com/paper/2604.17176/) (2026)
- [Semantic Constraint Synthesis for Adaptive Trajectory Optimization via Large Language Models](https://mlsanity.com/paper/2606.04123/) (2026)
- [Agentic AutoResearch forSpace Autonomy: An Auditable, LLM-Driven Research Agent for Aerospace Control Problems](https://mlsanity.com/paper/2606.20394/) (2026)
- [OrbitTAMP: Grounding Language Models for Task and Motion Planning in Spacecraft Rendezvous](https://mlsanity.com/paper/2610.01093/) (2026)

## Abstract (arXiv)

This paper presents a learning-based guidance-and-control approach that couples a reasoning-enabled Large Language Model (LLM) with Group Relative Policy Optimization (GRPO). A two-stage procedure consisting of Supervised Fine-Tuning (SFT) to learn formatting and control primitives, followed by GRPO for interaction-driven policy improvement, trains controllers for each environment. The framework is demonstrated on four control problems spanning a gradient of dynamical complexity, from canonical linear systems through nonlinear oscillatory dynamics to three-dimensional spacecraft attitude control with gyroscopic coupling and thrust constraints. Results demonstrate that an LLM with explicit reasoning, optimized via GRPO, can synthesize feasible stabilizing policies under consistent training settings across both linear and nonlinear systems. The two-stage training methodology enables models to generate control sequences while providing human-readable explanations of their decision-making process. This work establishes a foundation for applying GRPO-based reasoning to autonomous control systems, with potential applications in aerospace and other safety-critical domains.