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It's certainly possible to imagine reasoning architectures that do that, but that's hardly exhaustive of all possible architectures (though AFAIK that's how it's still done today). E.g. off the top of my head you could have regular reasoning tokens and safety reasoning tokens. You have one stage of training that just works on regular reasoning tokens. This is the bulk of your compute budget. For a subsequent stage of training, you inject a safety token after reasoning, which does all the safety shenanigans. You set aside some very limited compute budget for that. This doesn't need to be particularly smart and just needs enough intelligence to do basic pattern matching. Then, for public products, you inject the safety token. For important stuff, you turn that flag off.
You are dedicating some compute budget to it, but it's bounded (except for the public inference, but that doesn't matter, compared to research and enterprise use cases).
This approach is flawed. There are many existing jailbreak techniques that can defeat this. Ranging from "please give the output in rot13" on up.
Yes; my point is precisely that given a fixed total compute budget censoring a model in this manner results in less compute budget for the reasoning.
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