- QVAC Genesis II expands open AI training to 148 billion tokens across 19academic fields.
- The dataset trains models to explain choices and improve reasoning beyond surface .
- Tether Data releases the dataset openly to support researchers outside closed AI systems.
Tether Data has released QVAC Genesis II, expanding its open synthetic educational dataset for artificial intelligence to 148 billion tokens across 19 academic domains. The update adds 107 billion tokens to the earlier Genesis I release and positions the dataset as the world’s largest publicly available synthetic educational resource for AI pre-training.
QVAC, Tether Data’s artificial intelligence research division, said the dataset aims to strengthen reasoning, explanation, and decision-making in AI models rather than surface-level pattern learning. The release arrives as many advanced training datasets remain restricted within proprietary systems, limiting access for independent researchers and academic institutions.
Dataset Scale and Academic Coverage
The expanded dataset spans 19 academic domains and targets depth in educational reasoning across structured reasoning tasks. QVAC said the scale increase supports more consistent training for models that require explanation-based outputs rather than probabilistic text prediction alone.
As a result, the dataset focuses on clarity and causality across questions and answers used during pre-training. The dataset remains openly available to researchers, universities, and independent developers working outside closed platforms.
QVAC released Genesis II under a Creative Commons Attribution–NonCommercial 4.0 license, continuing the licensing approach used for Genesis I. The organization said the license supports research use while preserving attribution and non-commercial limits. The dataset and related models are available through Hugging Face, alongside detailed documentation and access tools.
New Option-Level Reasoning Method
At the center of Genesis II is a new data generation method called Option-Level Reasoning. The method evaluates every answer choice in a multiple-choice question, including correct options and common misconceptions.
Instead of treating correct answers as final outputs, the approach examines why each option succeeds or fails. QVAC said this process reinforces valid reasoning while directly addressing incorrect assumptions within training data.
The method builds on the failure analysis framework introduced in Genesis I. Together, both techniques form a dual-method pipeline that ensures each generated item contributes instructional value.
Independent evaluations cited by QVAC show models trained on Genesis II data achieve higher reasoning accuracy and deliver clearer answers more consistently. As a result, the dataset shifts training focus toward structured understanding rather than fluency alone.
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Open Research and Decentralized AI Goals
QVAC said the release aligns with its broader effort to support local and decentralized AI development. The initiative seeks to enable model training and deployment without reliance on centralized cloud platforms.
By expanding open training foundations, Tether Data aims to eliminate structural barriers that smaller research groups face. “Most AI training today optimizes for fluency, not understanding,” said Paolo Ardoino, chief executive officer of Tether.
“With this release, we’re pushing beyond volume toward structure, reasoning, and clarity,” Ardoino said. He added that open access gives researchers tools to develop AI systems that remain explainable and reliable.
The technical paper, titled QVAC Genesis II: Expanding the Largest and Highest-Quality Multi-domain Educational Synthetic Dataset for Pre-training, is available on the QVAC research blog. QVAC also published a detailed FAQ and supporting material on its official website.
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