Pretraining data dictates LLM scaling laws, study finds

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

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IMPACT Highlights the critical role of pretraining data in LLM performance, guiding future research and development efforts.

RANK_REASON Academic paper detailing research findings on LLM scaling laws. [lever_c_demoted from research: ic=1 ai=1.0]

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• Llama
• LLMs
• Mamba
• Prasanna Mayilvahanan
• Transformer

• paper
• model release