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

2025-09-01

Summary

The article presents a novel framework, Think-In Games (TiG), which enables large language models (LLMs) to improve procedural understanding through interaction with game environments using reinforcement learning. TiG leverages LLMs' reasoning capabilities and reformulates decision-making as a language modeling task, allowing LLMs to generate language-guided strategies that are refined through environmental feedback. This method bridges the gap between declarative and procedural knowledge, achieving competitive performance with less data and computational resources than traditional reinforcement learning (RL) methods.

Why This Matters

The TiG framework addresses a crucial challenge in AI development—bridging the gap between knowing about something and knowing how to do it. By enhancing LLMs' procedural understanding in interactive environments like games, this approach can lead to more efficient and interpretable AI systems. It signifies a shift in how AI can be trained to reason and act in dynamic and complex settings, paving the way for applications beyond gaming, including robotics and real-world decision-making scenarios.

How You Can Use This Info

Professionals in fields such as AI development, gaming, and robotics can leverage the insights from TiG to build systems that require both understanding and action in dynamic environments. This framework can be particularly useful for developing AI that needs to explain its actions, enhancing transparency and trustworthiness in AI systems. Additionally, by reducing data and computational requirements, TiG offers a more scalable and efficient approach to training AI models, potentially lowering costs and accelerating development timelines.

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