Getting video models to learn better, faster
This technical blog post discusses strategies for improving generative video models, specifically focusing on data filtering and quality control. The authors explain how moving away from 'more data' to 'better data' has improved their model's performance.
Why it matters
As generative AI matures, the focus is shifting from raw data volume to data curation, which is critical for the efficiency and quality of future AI models.
Getting Video Models to Learn Better, Faster
Road to Linum v3 · Issue 01 Since shipping Linum v2 in January , we've been heads down working on our next open-weight generative video model. For Linum v3, we're targeting three key improvements: better prompt adherence, training/inference acceleration, and consistent physics. In this series of blogs, we'll be breaking down both the engineering work and fundamental research that goes into training text-to-video models from scratch.
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