The three distinct stages of building a modern artificial intelligence
In short: Understanding how AI models transition from raw data to useful assistants requires looking past the hype. This video breaks down the technical pipeline of pre-training, mid-training, and post-training.
The video features machine learning experts Nathan Lambert and Sebastian Raschka, who explain the lifecycle of a large language model. It explores the progression from pre-training—where models learn general patterns from massive datasets—to mid-training and post-training, which refine those capabilities for specific tasks.
To explain the engineering required for creating dependable, practical systems, the Allen Institute for AI's post-training head, Lambert, joins Raschka, who wrote 'Build a Large Language Model (From Scratch).’
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Sources
- Summary by Intellect · source: How AI is trained: Pre-training, mid-training, and post-training explained | Lex Fridman Podcast
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