The scaling of models has worked consistently, and it's incredible that making these models bigger leads to predictable improvements in their performance.

From day one, we built our own foundation models instead of just fine-tuning existing ones, which allowed us to make significant architectural decisions during the pre-training process.

Our platform not only evaluates models but also helps partners select the best-performing models based on user feedback.

Our platform not only evaluates models but also helps partners select the best-performing models based on user feedback.

You need big models to train smaller models effectively, and while we have efficient smaller models, they often rely on insights from larger models.

Instead of just scaling up model size during training, researchers are likely to focus on scaling the compute available for the model's Chain of Thought.

With large models, you can't predict how users will interact with them, so it's crucial to observe their usage to understand successes and failures.

As we gather more data and build a larger platform, we can create more granular and personalized evaluations of AI models.

The scaling laws paper revealed that increasing parameters, data, and compute leads to a smooth, consistent improvement in model performance, demonstrating that performance is more dependent on scale than on the algorithm used.