Summary of Loss-to-loss Prediction: Scaling Laws For All Datasets, by David Brandfonbrener et al.
Loss-to-Loss Prediction: Scaling Laws for All Datasets
by David Brandfonbrener, Nikhil Anand, Nikhil Vyas, Eran Malach, Sham Kakade
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, the authors develop a strategy for predicting loss values from one dataset to another, applicable to pre-training datasets and downstream task data. By deriving simple shifted power law relationships between train losses, test losses, and compute scales, they demonstrate that these predictions extrapolate well across different distributions and tasks. The results show that scaling laws can be extended to predict loss values for models trained on various datasets and used for diverse applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to predict loss values when training a model on one dataset versus another. By finding simple patterns between train losses, test losses, and compute scales, the authors create a way to make predictions that work well even if we’re dealing with very different datasets or tasks. This is important for using models in new situations where we might not have enough data. |
Keywords
» Artificial intelligence » Scaling laws
