arXiv:2502.12120v3 Announce Type: replace Abstract: Scaling laws guide the development of large language models (LLMs) by offering estimates for the optimal balance of model size, tokens, and compute. More recently, loss-to-loss scaling laws that relate losses across pretraining datasets and downstream tasks have emerged as a powerful tool for understanding and improving LLM performance and generalization. In this work, we investigate which factors most strongly influence loss-to-loss scaling. Our experiments reveal that the pretraining data determines the scaling trend. In contrast, model siz
The proliferation of open-source and proprietary LLMs, alongside increased research into their underlying mechanisms, brings us closer to understanding optimal training strategies.
This research provides critical insights for optimizing LLM development, directly influencing the efficiency and effectiveness of resource allocation in an increasingly compute and data-intensive AI landscape.
The focus for improving LLM performance shifts more demonstrably towards quality and characteristics of pretraining data, rather than solely model size or compute at later stages.
- · Data curation platforms
- · Organizations with proprietary, high-quality datasets
- · Researchers specializing in data-centric AI
- · LLM developers solely focused on brute-force scaling
- · Generative AI models trained on low-quality data
- · Data brokers selling undifferentiated datasets
Increased investment in data collection, cleaning, and augmentation for LLM pretraining.
New competitive advantages will emerge for organizations that can secure and process domain-specific, high-quality data at scale.
The development of bespoke datasets tailored to specific applications or languages could lead to highly specialized and performant LLMs, further fragmenting the LLM market.
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