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Bookmark or Share- [2001.08361] Scaling Laws for Neural Language Models - arXiv.orgWe study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size, dataset size, and the amount of compute used for training, with some trends spanning more than seven orders of magnitude.
- The three AI scaling laws and what they mean for AI infrastructureThe evolution of AI scaling laws—from the foundational trio identified by OpenAI to the more nuanced concepts of post-training and test-time scaling championed by NVIDIA—underscores the complexity and dynamism of modern AI. These laws not only guide researchers and practitioners in building better models but also drive the design of the AI ...
- Scaling laws for neural language models - OpenAIWe study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size, dataset size, and the amount of compute used for training, with some trends spanning more than seven orders of magnitude.
- Neural scaling law - WikipediaIn machine learning, a neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down. These factors typically include the number of parameters, training dataset size, [1] [2] and training cost.
- A brief history of LLM Scaling Laws and what to expect in 2025The "Kaplan" Scaling Laws (OpenAI, 2020) suggest that as your pre-training compute budget increases, you should scale model size more than data. This means: given a 10x increase in training budget, one should scale model size by 5.5x and data by 1.8x.
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