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arXiv · 2603.00173

Summer-22B: A Systematic Approach to Dataset Engineering and Training at Scale for Video Foundation Model

Simo Ryu·Chunghwan Han
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Abstract

We describe our experience training Summer-22B, a video foundation model developed from scratch. This report documents the engineering challenges, design decisions, and lessons learned while scaling from raw footage collection to a functional model trained on approximately 50 million clips. We outline our approach combining metadata-driven dataset curation, multi-stage filtering, $\mu$P parameterization, and hypersphere-constrained optimization. We developed the Lavender Data system for dataset management and adopted inference-aware architectural choices. We share observations on what worked in our setting: dataset engineering consumed the majority of effort, architectural variants showed smaller differences than we expected, and $\mu$P hyperparameter transfer appeared effective even under geometric constraints. We hope this account proves useful to others undertaking similar projects.

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Simo Ryu, Chunghwan Han. 2026-02-26. Summer-22B: A Systematic Approach to Dataset Engineering and Training at Scale for Video Foundation Model. https://arxiv.org/abs/2603.00173

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