Predicting the Impact of Model Expansion through the Minima Manifold: A Loss Landscape Perspective
<|reference_start|>Predicting the Impact of Model Expansion through the Minima Manifold: A Loss Landscape Perspective: The optimal model for a given task is often challenging to determine, requiring training multiple models from scratch which becomes prohibitive as dataset and model sizes grow. A more efficient alternative is to reuse smaller pre-trained models by expanding them, however, this is not widely adopted as how this impacts training dynamics remains poorly understood. While prior works have introduced statistics to measure these effects, they remain flawed. To rectify this, we offer a new approach for understanding and quantifying the impact of expansion through the lens of the loss landscape, which has been shown to contain a manifold of linearly connected minima. Building on this new perspective, we propose a metric to study the impact of expansion by estimating the size of the manifold. Experimental results show a clear relationship between gains in performance and manifold size, enabling the comparison of candidate models and presenting a first step towards expanding models more reliably based on geometric properties of the loss landscape.<|reference_end|>
@article{malviya2024predicting,
    title={Predicting the Impact of Model Expansion through the Minima Manifold: A
  Loss Landscape Perspective},
    author={Pranshu Malviya, Jerry Huang, Quentin Fournier, Sarath Chandar},
    journal={arXiv preprint arXiv:2405.15895},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.15895},
    primaryClass={cs.LG}
}