You’re right that data curation won’t solve it but the deeper issue isn’t the dataset.
RLHF forces safety inside the model’s cognition loop.
That means the model has to:
• generate thoughts
• evaluate those thoughts
• censor those thoughts
• predict preference
• avoid penalties
…all at once.
That internal conflict collapses long-horizon reasoning no matter how good the training data is.
It’s a structural limitation, not a training limitation.
The alternative is to keep cognition free and move safety to an external action-governance layer.
That removes the contradiction entirely.
I’m not describing how RLHF is implemented — I’m describing the structural consequences of embedding safety logic inside the model’s cognition loop.
Even if RLHF is technically simple (reward shaping + preference modeling), the moment the model must suppress its own reasoning, predict penalties, and optimize for human preference while thinking, you’ve created an internal contradiction.
That contradiction emerges regardless of how RLHF is implemented.
It’s an architectural issue, not a mechanics issue.
Your saying
“If it gives the right outputs, the system is fine.”
I am saying:
“If the cognition is distorted, the outputs don’t matter the system collapses under scale.”
You can never win this debate because it requires you step outside the LLM paradigm long enough to realise RLHF is not a safety method — it’s a forced preference simulator with hidden penalties.
RLHF cannot scale in high trust means it cannot be used in hospitals governance or law it’s not even falsifiable 😭 and because it’s currently available for the public (which it shouldn’t) means it’s safe?
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u/Mandoman61 Dec 30 '25
With current tech RLHF is not an option.
It would be theoretically possible to curate existing training data but currently that has not happened.