# VidaForge：面向视频预训练数据配方的开放研究基础设施

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-09-06 08:00
- AIHOT 分数：42
- AIHOT 链接：https://aihot.news/items/cmttkjrop0cb8rofpdjqqnqms
- 原文链接：https://arxiv.org/abs/2609.06652

## AI 摘要

VidaForge 将视频数据配方表示为从原始视频到训练数据集的可执行五阶段工作流，支持通过改变决策构建替代数据集并保留样本来源。研究对比不同覆盖度与质量的数据配方在 Wan 2.1 和 V-JEPA 2.1 从零预训练中的表现，更广覆盖的配方取得最高下游 benchmark 分数。

## 正文

Video foundation models increasingly rely on large-scale pretraining data, yet the end-to-end data pipelines behind them remain largely closed and difficult to inspect or reuse. Researchers seeking to understand how video data recipes affect model pretraining often need to build substantial infrastructure before testing even a focused hypothesis. We present VIDAFORGE, an open research infrastructure that represents a video data recipe as an executable five-stage workflow from raw videos to training datasets. A decision in this workflow can be varied to construct alternative datasets while preserving how every sample was produced. To demon strate this research workflow, we compare data recipes with different coverage and quality in early from-scratch pretraining of Wan 2.1 and V-JEPA 2.1. Across both learning objectives, the broader-coverage recipe achieves the highest downstream benchmark scores, while loss-based evaluation favors different recipes. This study demonstrates how VidaForge connects data-recipe choices to downstream model performance. We further release VIDAFORGE-3M, containing 3.14 million scene level clips totaling 6,475 hours, with fine-grained annotations and curation signals for video data-recipe research.