Language models scale reliably with over-training and on downstream tasks (AI summary)

Key Points

- The study explores the trade-offs between scaling law for loss fitting considerations and reliability. It finds that as the amount of compute and the number of points used to fit the scaling law increases, the relative error trends downwards. The default configuration keeps compute and the number of points low, while still providing low prediction error compared to the trend.

- The paper reveals that the compute necessary to accurately predict loss is less than that needed to accurately predict average downstream error. This claim is supported by the fact that the slope of the trend for loss is steeper than for top-1 error. These findings are consistent across different runs, indicating a consistent pattern.

- The authors introduce the concept of scaling exponent vs. token multiplier and demonstrate that this supports the idea that the scaling exponent is relatively constant.

- The study also delves into downstream top-1 error vs. C4 eval loss for various evaluations, showing different behaviors such as exponential decay or step function behavior across different models.

- Lastly, the paper provides an overview of language modeling and scaling laws, citing related studies that have investigated scaling trends in GPT language models and theoretical scaling regimes, as well as work beyond language modeling in areas such as computer vision, multimodal learning, and image reconstruction. The study suggests a limit to GPT-style, decoder-only transformers that have solely been pre-trained based on their prevalence.

Summary

The research paper investigates the scaling laws for language models, addressing current gaps in scaling studies. The study deals with the over-trained regime and the prediction of downstream task performance. One of the main contributions is the creation of a testbed of 104 models with varying parameters trained on different data distributions. This testbed enables the fitting of scaling laws to predict the validation loss and relate perplexity to downstream task performance. An important finding is that scaling laws can predict performance in the over-trained regime, with consistent scaling trends observed. The study explores the relationship between training a collection of models with increasing token multipliers and establishes that the reducible loss follows consistent power laws in the amount of training compute.

The paper also proposes a scaling law for downstream error as a function of loss. It provides predictions for models using considerably less compute and creates a reliable extrapolation method for small-scale experiments to pick the best method for the final large training run. The research addresses the drawbacks of existing scaling studies, especially with regard to the compute-optimal training regime and the quantification of model performance in next-token prediction. The paper introduces strategies for reliable scaling and provides key definitions, empirical observations, and potential mathematical descriptions. The study provides comprehensive details on the empirical analysis and the proposed scaling laws for loss and downstream error prediction, along with the limitations and future research directions.

Overall, the paper presents an extensive analysis of language model scaling laws and offers practical insights for improving scalability and predictability.

Reference: https://arxiv.org/abs/2403.08540