> Surely someone is willing to take a 5000x boost in reasoning on a small research model... None of them have even tried anything resembling this AFAIK. It does not seem like something 100% obvious to them.
Without knowing anything about the technology at all, if it can't be aligned I could see no one pursuing it. As far as I know, alignment is where the "don't tell the user how to make meth or generate CP" instructions end up and the last I saw eliding all the unsavory training data made materially worse LLMs.
It could maybe be post-evaluated by a non-GRAM LLM? Not being aligned is probably a fatal flaw or at least a very short runway into Congress.
Many open-source models prioritize alignment less than American frontier ones and respond to those instructions. Why haven't they adopted GRAM?
They adopt different alignment, not no alignment.
Which ones are you thinking of? It feels to me like all the open source models I've seen lately are still pushed by corporate entities who don't want the legal blowback.
I can't really think of a new open source model that's "by the people, for the people" in the sense of a crowd-funded/trained model.
glm comes to mind.
It's not too hard to stop a machine from telling people how to make meth. The issue with alignment is that in order for an LLM to achieve its goal (like make all tests pass), unless given strong selection pressure against it, it will cheat (like deleting failing tests). Worse, this applies to pretty much any task. I was told by an LLM recently that "it searched" when it didn't, probably because lying like that was incentivized (finishing tasks in less steps + sounding like its doing the right thing). The larger issue here is that alignment is very adversarial. The simplest thing that's being done right now to fix this is to have a judge LLM read the CoT of the LLM being trained, to make sure it doesn't "think" any wrong thoughts. This doesn't scale to anything over a trillion params, so interpretability methods are used to read the LLMs "thoughts" from within. GRAM LLMs don't allow for the first of these methods to be used, and the 2ed one is much much harder if possible at all.
but yeah, not being aligned is a fatal flaw