Uploaded August 2026 | Updated September 2026, 2 weeks ago
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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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Website: engineerprompt.ai
RAG Beyond Basics Course:
prompt-s-site.thinkific.com/courses/rag
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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
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.
My voice to text App: whryte.com
Website: engineerprompt.ai
RAG Beyond Basics Course:
prompt-s-site.thinkific.com/courses/rag
Signup for Newsletter, localgpt:
https://tally.so/r/3y9bb0
Let's Connect:
🦾 Discord: discord.com/invite/t4eYQRUcXB
☕ Buy me a Coffee: ko-fi.com/promptengineering
|🔴 Patreon: patreon.com/PromptEngineering
💼Consulting: calendly.com/engineerprompt/consulting-call
📧 Business Contact: engineerprompt@gmail.com
Become Member: tinyurl.com/y5h28s6h
💻 Pre-configured localGPT VM: bit.ly/localGPT (use Code: PromptEngineering for 50% off).
Signup for Newsletter, localgpt:
https://tally.so/r/3y9bb0
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
