The era of scaling pre-training may not be completely over, but reasoning now appears to offer the most potential for maximizing gains from scaling compute.

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.

The diminishing returns in pre-training mean that significant increases in intelligence will require exponentially more compute resources, making it harder to achieve breakthroughs.

Scaling and training very large neural networks has proven effective, as demonstrated by our training of a neural network 60 times larger than previous models using 16,000 CPU cores.

The scaling of compute resources will vary significantly depending on the problem, with some requiring up to 10,000 times more compute than others, which allows for more capable models.

When I was in college, the idea that scaling compute in transformers could lead to artificial general intelligence was considered crazy, but I eventually recognized that scaling does improve performance.

The scaling laws became a foundational principle for AI development after being popularized by researchers, including those at OpenAI and Google DeepMind, who emphasized that it's not just about making models larger, but also about training them on sufficient data.

There is ongoing debate in the AI community about whether we have reached the limits of scaling laws, as the latest models have become larger and more expensive, yet their capabilities seem to be plateauing.

The scaling laws observed in AI suggest that larger models trained longer lead to more compression and intelligence, which is supported by the philosophical grounding of these concepts.