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Large language models (LLMs) are capable of developing an understanding of reality through language alone, without direct physical experience. Researchers conducted a study using Karel puzzles to test LLMs' ability to generate instructions for a robot in a simulated environment. The findings indicate that LLMs can create internal simulations and improve their problem-solving abilities over time.
The study revealed that LLMs' understanding of language evolves similarly to a child's language acquisition process. As training progresses, LLMs not only solve specific puzzles but also generalize their understanding to new challenges. This capability has significant implications for various AI applications, including robotics and natural language processing.
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