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this post was submitted on 18 Sep 2026
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in a manner of speaking - you always end up there. not by design though. models operate via continuous refinement and you can only optimize a model so much until it is a mess and you need to figure out where to roll back. so you either get shit like semantic drift or variance decay and you can whack a mole it to an extent but then you hit the rlhf wall when the model starts gaming its reinforcement framework and the fat lady sings.
Well put. Never thought of it that way.
the only more or less workable way to keep it under control is maintaining a closed loop small-scale environment - kinda like NotebookLM where you upload documents and that's all there is to work with - outside of that it is a mess.