The proposed scaling law's effectiveness can be influenced by the quality and composition of the training dataset. Higher-quality data may require fewer tokens per parameter for optimal performance, as indicated by previous research [9]. When dealing with a combination of high-quality synthetic and real-world data, the optimal token-per-parameter ratio can shift, impacting the allocation of compute resources. In such cases, the relationship between model size, dataset size, and compute expenditure may deviate from the linear scaling law proposed in the study. The composition of the dataset, including the diversity, relevance, and representativeness of the data, plays a crucial role in determining the efficiency and effectiveness of the training process. Therefore, variations in data quality and composition can introduce complexities that may challenge the straightforward application of the proposed scaling law.