Stanford CS224V Lecture 14: Scaling Language Models When Data Is the Bottleneck
The final lecture is not a complete LLM-training tutorial. It studies data efficiency under fixed data and abundant compute, revisiting epochs, batches, ensembles, self-training, and conditions for synthetic continued pretraining.