Training large models is challenging because new issues arise that weren't anticipated at the project's inception, requiring ongoing adjustments and problem-solving.

Post-training focuses on developing model personality, which requires substantial human effort to create appealing and effective agent behaviors.

We are no longer in a compute-constrained environment for our best models, marking a significant shift in our approach to model training and development.

DeepSeek's approach to AI training reflects a broader trend in the industry where companies are increasingly focused on optimizing both training and inference processes to enhance model performance.

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.

While specialized models can be effective for specific tasks, the overall trend is towards general models that incorporate learnings from specialized iterations.

The trend in machine learning is towards convergence, with many different types of models shifting to a more unified transformer model.

To effectively remove facts from a model, it must be done at every stage of the training process, including pre-training and post-training adjustments.

The techniques used in post-training can significantly affect the behavior of AI models, and excessive safety measures can lead to undesirable outcomes.