Update README.md

README.md

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 ## 1. Dataset Summary
 This dataset is dynamically constructed using the **GAMEBoT** framework in the **Connect4** game. It is designed to evaluate Large Language Models' (LLMs) ability in symbolic reasoning, board state parsing, and lookahead planning. 
 
-By serializing $6 \times 7$ grid states into text-based formats, this dataset challenges models to identify winning topologies in a deterministic environment with a state-space complexity of approximately $4.5 \times 10^{12}$, ensuring a robust evaluation against data contamination.
+By serializing 6 &times; 7 grid states into text-based formats, this dataset challenges models to identify winning topologies in a deterministic environment with a state-space complexity of approximately 4.5 &times; 10<sup>12</sup>, ensuring a robust evaluation against data contamination.
 
 ## 2. Construction Methodology
 - **State Generation**: Two randomized agents simulated gameplay to generate a diverse distribution of board states, covering both balanced and critical tactical scenarios.