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arXiv · 2605.10129

Synthetic Pre-Pre-Training Improves Language Model Robustness to Noisy Pre-Training Data

Xu Guo·Runyu Peng·Jian Tong·Yunhua Zhou·Haijun Lv·Zhihui Lu·Qipeng Guo
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Abstract

Large language models (LLMs) rely on web-scale corpora for pre-training. The noise inherent in these datasets tends to obscure meaningful patterns and ultimately degrade model performance. Data curation mitigates but cannot eliminate such noise, so pre-training corpora remain noisy in practice. We therefore study whether a lightweight pre-pre-training (PPT) stage based on synthetic data with learnable temporal structure helps resist noisy data during the pre-training (PT) stage. Across various corruption settings, our method consistently improves robustness to noise during PT, with larger relative gains at higher noise levels. For a 1B-parameter model, a synthetic PPT stage with only 65M tokens achieves the same final loss as the baseline while using up to 49\% fewer natural-text PT tokens across different noise levels. Mechanistic analyses suggest PPT does not immediately suppress attention to noisy tokens. Rather, PPT-initialized models gradually downweight attention between corrupted tokens during noisy PT. This indicates that synthetic PPT inhibits noise self-modeling and shapes the subsequent optimization trajectory. Code is available at https://github.com/guox18/formal-language-prepretraining.

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Xu Guo, Runyu Peng, Jian Tong, Yunhua Zhou, Haijun Lv, Zhihui Lu, Qipeng Guo. 2026-05-11. Synthetic Pre-Pre-Training Improves Language Model Robustness to Noisy Pre-Training Data. https://arxiv.org/abs/2605.10129

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