Enhancing military decision-making through simulation-guided post-training of Large Language Models

The capabilities of Large Language Models (LLMs) have rapidly evolved, enabling them to perform increasingly complex reasoning tasks. However, while their general reasoning abilities are shaped during large-scale pretraining, domain-specific reasoning such as tactical decision-making in military contexts requires dedicated post-training. This paper introduces a simulation-guided Reinforcement Fine-Tuning (RFT) approach in which reward signals are derived from the outcomes of a combat simulation environment. By embedding a military-grade simulator into the RFT loop via the Group Relative Policy Optimization (GRPO) algorithm, model outputs are evaluated based on tactical effectiveness rather than human annotations or rule-based correctness. A proof-of-concept study demonstrates that the method significantly improves the feasibility and tactical quality of generated Courses of Action (COAs), even under limited training schedules. These findings establish simulation-guided RFT as a promising direction for equipping LLMs with tactically relevant reasoning skills and pave the way toward next-generation decision-support systems in military environments.