As large language models (LLMs) are increasingly deployed in high-stakes settings, the risk of generating harmful or toxic content remains a central challenge. Post-hoc alignment methods are brittle: once unsafe patterns are learned during pretraining, they are hard to remove. We present a data-centric pretraining framework that builds safety into the model from the start. Our contributions include: (i) a safety classifier trained on 10,000 GPT-4 labeled examples, used to filter 600B tokens; (ii) the largest synthetic safety dataset to date (100B tokens) generated via recontextualization of harmful web data; (iii) RefuseWeb and Moral Education datasets that convert harmful prompts into refusal dialogues and web-style educational material; (iv) Harmfulness-Tag annotations injected during pretraining to flag unsafe content and steer away inference from harmful generations; and (v) safety evaluations measuring base model behavior before instruction tuning. Our safety-pretrained models reduce attack success rates from 38.8% to 8.4% with no performance degradation on standard LLM safety benchmarks.
View on arXiv@article{maini2025_2504.16980,
title={ Safety Pretraining: Toward the Next Generation of Safe AI },
author={ Pratyush Maini and Sachin Goyal and Dylan Sam and Alex Robey and Yash Savani and Yiding Jiang and Andy Zou and Zacharcy C. Lipton and J. Zico Kolter },
journal={arXiv preprint arXiv:2504.16980},
year={ 2025 }
}