MixtureVitae: Open Web-Scale Pretraining Dataset With High Quality Instruction and Reasoning Data Built from Permissive-First Text Sources

Huu Nguyen, Victor May, Harsh Raj, Marianna Nezhurina, Yishan Wang, Yanqi Luo, Vu Minh Chien, Taishi Nakamura, Ken Tsui, Van Khue Nguyen, David Salinas, Aleksandra Krasnodębska, Christoph Schuhmann, Mats Leon Richter, Xuan-Son Vu, Jenia Jitsev

Keywords: Large Language Models, Pretraining Datasets, Permissive Licensing, Open Data for AI, Text and Code Corpora, Reproducibility

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.

Abstract: We present MixtureVitae, an open‑access pretraining corpus built to minimize legal risk while providing strong downstream performance. MixtureVitae follows a permissive‑first, 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). MixtureVitae adopts a simple, single-stage pretraining recipe that integrates a large proportion of permissive synthetic instruction and reasoning data—signals typically introduced during post-training and generally scarce in permissive web corpora. We categorize all sources into a three-tier scheme that reflects varying risk levels and provide shard-level provenance metadata to enable risk-aware usage. In controlled experiments using the open‑sci‑ref training protocol (fixed architectures and hyperparameters; 50B and 300B token budgets across 130M–1.7B parameters), models trained on MixtureVitae consistently outperform other permissive datasets across a suite of standard benchmarks, and at the 1.7B-parameters/300B-tokens setting, they surpass FineWeb‑Edu and approach DCLM late in training. Performance is particularly strong on MMLU and on math and code benchmarks: a 1.7B model pretrained on 300B MixtureVitae tokens matches or exceeds a strong 1.7B instruction‑tuned baseline on GSM8K, HumanEval, and MBPP, despite using over 36 fewer tokens (300B vs roughly 11T). Supported by a thorough decontamination analysis, these results show that permissive‑first data with high instruction and reasoning density, tiered by licensing and provenance-related risk, can provide a practical and risk-mitigated foundation for training capable LLMs, reducing reliance on broad web scrapes without sacrificing competitiveness.

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Submission Number: 4