Simple rules explain why bigger image and video models keep getting better
Scientists found a neat pattern: as you give a system more size and training, it gets better in a steady, predictable way.
This happens for photos, short videos, picture-and-text tools, and even for solving math problems.
The result is that bigger models usually mean predictable improvement, and you can roughly tell how big a system must be to reach a goal.
They even shows that huge systems become nearly perfect at tiny, low-res images, and you can forecast what size you need for better images at other resolutions.
The work also uncovers links between captions and pictures, so it answers, in part, whether a photo is worth a thousand words — the connection strengthens as models grow.
In math tasks the same rules help explain how well systems do when pushed beyond what they saw during training.
Altogether this points to simple scaling laws that guide progress, and offers a practical way to plan future systems, saving time and compute while getting smarter performance.
Read article comprehensive review in Paperium.net:
Scaling Laws for Autoregressive Generative Modeling
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