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[[{“value”:”Thanks to DataForSEO for sponsoring this video — get $5 in free credits (standard is $1): https://l.dataforseo.com/44MSLlT
AI Distillation Explained: Why It’s Misunderstood (and What It Means for Open Models)
I break down why “distillation” is widely misunderstood in AI and how that confusion could fuel policy moves like banning open Chinese models. Using Moonshot’s rapid K2 → K2.6 → K3 progress and claims that it was “distilled” from top US frontier models, I explain textbook logit distillation (soft labels, dark knowledge, temperature, and the need for access to logits), how this maps to LLMs, and why most APIs don’t expose the probability signals required for real capability transfer. I cover four types of distillation, focusing on sequence-level distillation/output harvesting (e.g., Alpaca/Vicuna, and Anthropic’s report of large-scale fraudulent Claude usage) and why it mostly copies style. I also walk through the modern training pipeline (pre-train, mid-train, SFT, RL), on-policy distillation/RLAIF requirements, compute, and timelines around Fable-5 vs K3.
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00:00 Distillatio
00:30 Moonshot K3 Timeline
01:30 What Distillation Means
02:41 Soft Labels Explained
05:11 Sponsor Break
06:11 Distilling Language Models
08:15 Four Distillation Types
09:49 Sequence Output Harvesting
11:55 Where Capability Comes From
13:28 On Policy Distillation
15:21 Can K3 Copy Fable 5?
16:37 Wrap Up”}]] Read More Prompt Engineering
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