Scaling Laws

In short

The observation that language models keep getting better as you give them more Data and make their architectures bigger — unlike previous models that usually hit a sweet spot.

When you train a Model, you’re feeding it Data and giving it enough parameters to learn from that data. The natural question is: what happens if you just keep adding more data and more parameters? That’s exactly what scaling laws describe.

This is what drove the LLM revolution. Previous model types would reach a point of diminishing returns — more data or bigger models wouldn’t help much. But LLMs broke that pattern. They just kept improving with scale. That’s how they went from a few million parameters to hundreds of billions. This is a key reason companies are pouring so much money into building bigger and bigger models.

Related

• LLMs - scaling laws apply primarily to LLMs
• Model Parameters - more parameters = bigger model
• Data - more data = better performance
• Training - scaling requires more training compute