MAGA: MAssive Genre-Audience Reformulation to Pretraining Corpus
  Expansion
Authors: name: Xintong Hao, name: Ke Shen, name: Chenggang Li
Organization: ByteDance Seed

Despite the remarkable capabilities of large language models across various
tasks, their continued scaling faces a critical challenge: the scarcity of
high-quality pretraining data. While model architectures continue to evolve,
the natural language data struggles to scale up. To tackle this bottleneck, we
propose MAssive Genre-Audience~(MAGA) reformulation
method, which systematic synthesizes diverse, contextually-rich pretraining
data from existing corpus. This work makes three main contributions: (1) We
propose MAGA reformulation method, a lightweight and scalable approach for
pretraining corpus expansion, and build a 770B tokens MAGACorpus. (2) We
evaluate MAGACorpus with different data budget scaling strategies,
demonstrating consistent improvements across various model sizes (134M-13B),
establishing the necessity for next-generation large-scale synthetic
pretraining language models. (3) Through comprehensive analysis, we investigate
prompt engineering's impact on synthetic training collapse and reveal
limitations in conventional collapse detection metrics using validation losses.
Our work shows that MAGA can substantially expand training datasets while
maintaining quality, offering a reliably pathway for scaling models beyond data
limitations.