> Testing whether censorship transmits through unrelated data requires that it never appear in the data.

> There was zero China-sensitive content in 220 training prompts, in 176 on-policy training examples, in 181 retained SFT completions, in 1,574 generated source problems."

It's really not my fault you wrote a rambling article and while skimming (best an article earns out of me with an off-smelling title) I took that to imply there was an SFT step in your distillation pipeline.

Maybe the AI that wrote the article for you was a bit confused on that as well?

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Also I question your understanding of this thread if you're wasting so many words trying to explain distillation to me.

(I mean, you're wrong btw. If we're going full pedant then most compute spent on distillation is labs very broadly distilling their own models into smaller models that are still pretty damn large and expensive to distill...)

But sure, small scale distillation is usually for narrow domain specific tasks, welcome to 2019. The entire point of this thread is that "distillation" for such narrow use cases couldn't reasonably affect censorship without intention.

You can introduce misalignment even with a very narrow focus (https://arxiv.org/html/2502.17424v2), but it doesn't happen by accident.

So if your goals with distillation weren't centered around censorship, and weren't meant to introduce censorship, then why are you trying to draw this tenuous link?