LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws
Researchers have identified that the pretraining data is the primary determinant of loss-to-loss scaling laws in large language models. Their experiments indicate that factors such as model size, optimization hyperparameters, and even architectural differences between Transformers and state-space models have a limited influence on these scaling trends. The findings suggest that curating appropriate pretraining datasets is crucial for optimizing downstream performance, while other model configurations can be adjusted for training efficiency. AI
IMPACT Highlights the critical role of pretraining data in LLM performance, guiding future research and development efforts.