# Scaling Data-Constrained Language Models

**Authors:** Niklas Muennighoff, Alexander M. Rush, Boaz Barak, Teven Le Scao, Aleksandra Piktus, Nouamane Tazi, Sampo Pyysalo, Thomas Wolf, Colin Raffel  
**arXiv:** [2305.16264](https://arxiv.org/abs/2305.16264)  
**Date:** 2023-05-25  
**Summary page:** https://www.summarizepaper.com/en/paper/2305.16264v4/

## Key points

- Authors: Niklas Muennighoff, Alexander M. Rush, Boaz Barak, Teven Le Scao, Aleksandra Piktus, Nouamane Tazi, Sampo Pyysalo, Thomas Wolf, Colin Raffel
- Trend of scaling language models by increasing parameter count and training dataset size
- Limitation: Finite amount of text data available on the internet for training
- Training with up to 4 epochs of repeated data has minimal impact on loss compared to using unique data
- Diminishing marginal value of additional compute as repetition level increases beyond threshold
- Proposed scaling law considering diminishing returns from repeated tokens and surplus parameters
- Strategies to mitigate data scarcity issues: augmenting training datasets with code data or removing commonly used filters
- Research outcomes accessible at https://github.com/huggingface/datablations

## Summary

In their paper titled "Scaling Data-Constrained Language Models," authors Niklas Muennighoff, Alexander M. Rush, Boaz Barak, Teven Le Scao, Aleksandra Piktus, Nouamane Tazi, Sampo Pyysalo, Thomas Wolf, and Colin Raffel delve into the current trend of scaling language models by increasing both parameter count and training dataset size. They highlight a potential limitation in this trend - the finite amount of text data available on the internet for training purposes. To address this challenge, the researchers explore scaling language models in data-constrained environments. Through a series of extensive experiments varying data repetition levels and compute budgets, with training datasets reaching up to 900 billion tokens and models with 9 billion parameters, the team makes significant findings. They observe that when faced with limited data for a fixed compute budget, training with up to 4 epochs of repeated data has minimal impact on loss compared to using unique data. However, as the level of repetition increases beyond this threshold, the marginal value of additional compute diminishes until it reaches zero. To provide a framework for optimizing compute resources in such scenarios, the authors propose and validate a scaling law that considers the diminishing returns from repeated tokens and surplus parameters. Additionally, they explore strategies to mitigate data scarcity issues by augmenting training datasets with code data or removing commonly used filters. The research outcomes from over 400 training runs are openly accessible through their repository at https://github.com/huggingface/datablations. This comprehensive study sheds light on effective approaches for scaling language models in resource-constrained settings and offers valuable insights for future advancements in natural language processing research.