Summary of A Hitchhiker’s Guide to Scaling Law Estimation, by Leshem Choshen et al.
A Hitchhiker’s Guide to Scaling Law Estimation
by Leshem Choshen, Yang Zhang, Jacob Andreas
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 The paper proposes an efficient method for estimating and interpreting scaling laws in machine learning. These laws predict the performance of a target model by extrapolating from easier-to-train models with fewer parameters or smaller training sets. The authors collect a large dataset of losses and downstream evaluations for 485 previously published pretrained models, then use this data to estimate over 1000 scaling laws and derive best practices for estimating them in new model families. They find that fitting scaling laws to intermediate checkpoints during training improves accuracy, and that estimates are generally most accurate when derived from similar-sized models. The paper also shows that while different model families have distinct scaling behaviors, they can often be predicted using a single model with the same architecture and scaling parameter estimates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us better understand how to predict the performance of machine learning models by analyzing how smaller models behave during training. This information can help researchers choose the best pre-training decisions for their models. The authors create a large dataset of existing models and use it to estimate over 1000 scaling laws, which are important for predicting how well a model will perform. |
Keywords
» Artificial intelligence » Machine learning » Scaling laws
