Synthetic data is increasingly used in language-model pretraining.
Existing knowledge-corpus synthesis pipelines, including WRAP and Nemotron-CC, usually take a source passage or document segment as input and generate rewritten or QA-style samples from that text.
This can improve surface form and prompt diversity, but it does not explicitly require fine-grained source facts -- especially secondary entities, list/table entries, and precise temporal attributes -- to appear in the synthesized corpus.
We propose AFCS (Atomic-Fact-Conditioned Synthesis), a pipeline that first extracts source-supported atomic facts and then uses each retained fact to drive generation.
AFCS uses structure-aware extraction over the document tree, source-grounded verification, local context expansion, and multi-format generation.
In continued-pretraining experiments with OLMo-3-7B and a 32B base on the same FineWiki source corpus, AFCS outperforms WRAP and Nemotron-CC on SimpleQA, SimpleQA Verified, PopQA, and HotpotQA.
Granularity ablations show that fact-level synthesis improves over chunk-level, paragraph-level, and entity-level synthesis;
and a 100B-token mid-training integration improves factual benchmarks and raises the 12-task general benchmark average by +1.09.