There are potential limitations and edge cases where the log-linear relationship between compute and model performance may not hold true. Extreme ratios between model size and training tokens, such as training a model with significantly fewer parameters on an excessively large number of tokens or vice versa, may not align with the proposed scaling law. In such cases, the linear correlation between compute expenditure and model performance may break down, leading to suboptimal results. Additionally, the applicability of the log-linear relationship could be constrained by the specific range of ratios between model size and dataset size. Beyond certain thresholds, the scaling law may not accurately predict the performance outcomes, highlighting the need for further research to identify the boundaries of its validity. These edge cases underscore the importance of considering the nuances and complexities of different training scenarios to ensure the scalability and effectiveness of language models.