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arXiv · 2509.15248

Synthetic bootstrapped pretraining

Zitong Yang·Aonan Zhang·Hong Liu·Tatsunori Hashimoto·Emmanuel Candès·Chong Wang·Ruoming Pang
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Abstract

We introduce Synthetic Bootstrapped Pretraining (SBP), a language model (LM) pretraining procedure that first learns a model of relations between documents from the pretraining dataset and then leverages it to synthesize a vast new corpus for joint training. While the standard pretraining teaches LMs to learn causal correlations among tokens within a single document, it is not designed to efficiently model the rich, learnable inter-document correlations that can potentially lead to better performance. We validate SBP by designing a compute-matched pretraining setup and pretrain a 3B-parameter and a 6B-parameter model on up to 1T tokens from scratch. We find SBP consistently improves upon a strong repetition baseline and delivers up to 60% of performance improvement attainable by an oracle upper bound with access to 20x more unique data. Qualitative analysis reveals that the synthesized documents go beyond mere paraphrases -- SBP first abstracts a core concept from the seed material and then crafts a new narration on top of it. Besides strong empirical performance, SBP admits a natural Bayesian interpretation: the synthesizer implicitly learns to abstract the latent concepts shared between related documents.

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Zitong Yang, Aonan Zhang, Hong Liu, Tatsunori Hashimoto, Emmanuel Candès, Chong Wang, Ruoming Pang. 2025-12-13. Synthetic bootstrapped pretraining. https://arxiv.org/abs/2509.15248

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