Reas

ReaS, or Recurrent Scaling, is a technique used in machine learning to improve models that process sequential data, like text or time series. It involves repeatedly applying a scaling process during training, allowing the model to better understand patterns that occur over multiple steps or periods. This approach helps the model learn more complex relationships and increases its accuracy in predicting or analyzing sequences. Essentially, ReaS enhances a model's ability to handle and interpret data that unfolds over time by incorporating repetitive, scaled learning cycles.