Think in Games: Learning to Reason in Games via Reinforcement Learning with Large Language Models

Exploring foci of: arXiv (Cornell University) Think in Games: Learning to Reason in Games via Reinforcement Learning with Large Language Models August 2025 • Liao Yi, Yu Gu, Yuan Sui, Zining Zhu, Yifan Lu, Guohua Tang, Zhongqian Sun, Wei Yang Large language models (LLMs) excel at complex reasoning tasks such as mathematics and coding, yet they frequently struggle with simple interactive tasks that young children perform effortlessly. This discrepancy highlights a critical gap between declarative knowledge (knowing about something) and procedural knowledge (knowing how to do something). Although traditional reinforcement learning (RL) agents can acquire procedural knowledge through environmental interaction, they often operate as black boxes and require… Open Article Page

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