Training Llama 2, 3 & 4: The Path to Open Source AGI — with Thomas Scialom of Meta AI

Latent Space · Beginner ·📄 Research Papers Explained ·1y ago
Llama 2 lead and Llama 3 post-training lead Thomas Scialom of Meta/FAIR, on the Chinchilla trap, why Synthetic Data and RLHF works, and how Llama4's focus on Agents will lead us to Open Source AGI. Chapters: 00:00:00 Introductions 00:04:16 The Llama Origin Story 00:07:34 Are there RLHF scaling laws? 00:09:56 Avoiding the "Chinchilla trap" 00:12:15 Why 405B? 00:14:27 FP8 training and other scaling research 00:17:48 Llama3 vs Llama2 00:18:32 Synthetic data for pre-training 00:21:43 Tool use to generate synthetic data 00:22:40 Pre-training data recipe 00:26:00 Why not MoE? 00:27:05 Why RLHF is s…
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Chapters (20)

Introductions
4:16 The Llama Origin Story
7:34 Are there RLHF scaling laws?
9:56 Avoiding the "Chinchilla trap"
12:15 Why 405B?
14:27 FP8 training and other scaling research
17:48 Llama3 vs Llama2
18:32 Synthetic data for pre-training
21:43 Tool use to generate synthetic data
22:40 Pre-training data recipe
26:00 Why not MoE?
27:05 Why RLHF is so important
37:06 How they eval models
41:50 Benchmaking Uncertainty
44:04 Structured output and tool calling
45:52 Llama4 & Agents
52:01 Will Meta keep releasing open models?
53:55 Why tokenizer vocab size is underrated
59:12 AI & Startups
1:03:13 Hiring at Meta AI

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