Title: Reasoning Capabilities of Large Language Models in Dynamic Games of Imperfect Information: A Case Study on Dou Dizhu
Primary area: foundation or frontier models, including LLMs
Keywords: Large Language Models (LLMs), Imperfect-Information Games, Strategic Reasoning, Curriculum Learning, Multi-agent Systems

Abstract: The performance of Large Language Models (LLMs) in dynamic games of imperfect information, which demand deep strategic reasoning, remains an under-explored area. This paper investigates this challenge using the popular card game Dou Dizhu as a representative testbed, aiming to enhance the reasoning and decision-making abilities of LLMs in such complex scenarios. First, we establish a rigorous and fair benchmark using a duplicate round-robin tournament to comprehensively evaluate the performance of several state-of-the-art LLMs. This evaluation provided a clear performance baseline and revealed that while these top-tier models are powerful, their significant computational cost motivates the development of smaller, more efficient alternatives. Furthermore, we propose a novel data construction framework designed to bridge the information gap. Its core consists of two unique data curation mechanisms tailored for such games: globally optimal decision alignment via symmetric information and real-time in-game feedback augmentation. By fine-tuning a smaller-scale model on a structured curriculum—comprising this curated data alongside victorious game data—we demonstrate a significant enhancement in gameplay proficiency. The model exhibits a substantially reduced decision error rate and a strategic robustness that significantly outperforms baseline models. Code is available in the Supplementary Material.

TL;DR: We fine-tune a small LLM on a novel dataset, curated via multi-agent feedback and "post-hoc validation" with perfect information, significantly enhancing its strategic foresight and reducing decision errors in dynamic games of imperfect information.