We implemented a parameter server technique to scale training by using multiple parallel copies of the model, which allows for efficient updates across large datasets.

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

We aim to reduce the time it takes to train models, allowing engineers to conduct multiple experiments simultaneously and improve workflow efficiency.

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.

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.

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.

DeepSeek's recent models, including V3, highlight the importance of balancing training and inference compute resources to optimize performance and efficiency.

While scaling pre-training may have plateaued, training test time compute could open up a new paradigm for scaling laws, unlocking previously unimaginable capabilities.

Resolving issues during the training run led to a significant performance boost, which energized the team and improved morale.