Getting video models to learn better, faster

Recent improvements in image and video models stem primarily from three data-focused strategies rather than architectural changes: better data filtering and rebalancing to remove noise, improved annotations using advanced LLMs for richer captions, and synthetic data generation from existing models. The field has shifted away from simply aggregating massive amounts of raw data, instead focusing on quality filtering—a technique that has evolved from traditional computer vision methods in 2024 to GPU-based fine-tuned LLMs and reinforcement learning approaches by late 2025. This data-centric approach allows models to learn more effectively by focusing their capacity on high-quality training examples rather than wasting resources on low-quality or noisy data.