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For instance, an initial prompt of merely ten frames of gameplay leads to the generation of sequences that mirror actual game mechanics. The sophistication of this technology is attributed to its robust training regime, progressing from 10,000 to over a million training updates to refine its accuracy and narrative quality.
Challenges in Development
Creating the Wham model involved overcoming substantial challenges in scaling its capabilities. Early training updates produced rough outputs, with gameplay sequences that were inconsistent. However, as training progressed, the generated sequences began to reflect a greater fidelity to actual gameplay, thereby enhancing their usefulness to developers.