<ul data-eligibleForWebStory="true"><li>DipLLM is a fine-tuned Large Language Model (LLM) designed for strategic decision-making in Diplomacy, a complex multiplayer game that combines cooperation and competition.</li><li>Traditional methods for AI in Diplomacy rely on equilibrium search, requiring extensive game data and computational resources.</li><li>LLMs offer an alternative by leveraging pre-trained knowledge for strong performance with limited fine-tuning.</li><li>However, applying LLMs to Diplomacy is challenging due to the game's complexity and strategic interactions among players.</li><li>DipLLM simplifies the task by using an autoregressive factorization framework to break down multi-unit action assignment into unit-level decisions.</li><li>The model fine-tunes by learning equilibrium policies and outperforms the Cicero model with only 1.5% of the training data.</li><li>This research demonstrates the potential of fine-tuned LLMs for complex strategic decision-making in multiplayer games.</li></ul>

DipLLM: Fine-Tuning LLM for Strategic Decision-making in Diplomacy

Discover more