Title: MixtureVitae: Open Web-Scale Pretraining Dataset With High Quality Instruction and Reasoning Data Built from Permissive Text Sources
Primary area: datasets and benchmarks
Keywords: Large Language Models, Pretraining Datasets, Permissive Licensing, Open Data for AI, Text and Code Corpora

Abstract: We present MixtureVitae, an open‑access pretraining corpus built to minimize copyright risk while preserving strong downstream performance. MixtureVitae follows a risk‑mitigated sourcing strategy that combines public‑domain and permissively licensed text (e.g., CC‑BY/Apache) with carefully justified low‑risk additions (e.g., government works and EU TDM‑eligible sources), alongside targeted instruction, reasoning and synthetic data with documented provenance. We detail a transparent, multi‑stage pipeline for license‑aware filtering, safety and quality screening, and domain‑aware mixing, and we release the dataset and curation recipes to support reproducible research. In controlled experiments using the open‑sci‑ref training protocol (fixed architectures at 130M/400M/1.3B/1.7B parameters; training budgets of 50B and 300B tokens), models trained on MixtureVitae consistently outperform other permissive datasets across a suite of standard benchmarks, and at the 1.7B/300B setting they surpass FineWeb‑Edu and approach DCLM in the later stages of training. Performance is particularly strong on MMLU and competitive on QA tasks. These results demonstrate that permissive‑first, risk‑mitigated data provides a practical and legally mitigated foundation for training capable LLMs, reducing reliance on indiscriminate web scraping without sacrificing competitiveness.

TL;DR: MixtureVitae is a legally mitigated pretraining dataset built from open and civic sources. It matches or beats mixed-license baselines, proving high performance doesn’t require risky web scrapes.