Navigating Scaling Laws: Compute Optimality in Adaptive Model Training

• URL: http://arxiv.org/abs/2311.03233v3
• Date: Thu, 23 May 2024 08:28:56 GMT
• Title: Navigating Scaling Laws: Compute Optimality in Adaptive Model Training
• Authors: Sotiris Anagnostidis, Gregor Bachmann, Imanol Schlag, Thomas Hofmann,
• Abstract summary: In recent years, the state-of-the-art in deep learning has been dominated by very large models that have been pre-trained on vast amounts of data. We extend the concept of optimality by allowing for an adaptive' model, i.e. a model that can change its shape during training.
• Score: 39.96209967632896
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, the state-of-the-art in deep learning has been dominated by very large models that have been pre-trained on vast amounts of data. The paradigm is very simple: investing more computational resources (optimally) leads to better performance, and even predictably so; neural scaling laws have been derived that accurately forecast the performance of a network for a desired level of compute. This leads to the notion of a `compute-optimal' model, i.e. a model that allocates a given level of compute during training optimally to maximize performance. In this work, we extend the concept of optimality by allowing for an `adaptive' model, i.e. a model that can change its shape during training. By doing so, we can design adaptive models that optimally traverse between the underlying scaling laws and outpace their `static' counterparts, leading to a significant reduction in the required compute to reach a given target performance. We show that our approach generalizes across modalities and different shape parameters.

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