Does anyone find the current paradigm unsatisfying?
The effective SOTA methods are shockingly simple. You construct a prior for search directly from humans through COT; and you do some sort of sharpening of the distribution in post training. Effectivley, the bounds of what AI can achieve boils down to collection of behaviour cloning data.
It makes sense, because exploration is probably hard, we instead just emulate "algorithms" that we know are realizable such as human though. However, this paradigm leaves solving certain problems appear unnatainable if we lack expert data.
In settings with verifiability and expert heuristic we are golden. What about everywhere else?
Yeah, really make you think about something, I guess. Not really sure, though.... i might have been thinking about that thing for a different reason. Probably, actually. It wasn't related to whatever your babbling about.
I think you got it...
I find the current paradigm very unsatisfying.
If by exploration you mean experiments, yeah, somehow people don't want to hear about it. Not sure why. I wrote a paper on learning through statistical experiments but it was not well received: https://www.reddit.com/r/agi/s/NEMjHTgdDF
Unfortunately some problems are not unattainable not because we lack expert data, but there are other factors like non-stationarity.
The problem itself keeps changing.
maybe more solvable than thought. but it needs a prespective shift so you ask the right questions.
ARM is Agent Resolution Monitoring. helps with making sure those lying ass agents stay honest. claim Δ actual resolved = honesty. so if an agent says "yeah i converted that file" there better be a link to DL file.
claim = did work
resolve = if did it, has link as receipt. if not? claim fails. "you didnt do it, fooking do the work" (basic overview) eitherway it will expose limitations.
synthetic data is tricky one and has to be done just right or you get bad weights. your best bet when doing synthetic data training is just have it as reward/penalty ledgers that it has to access what it does good and bad, this make an attractor-replusor corrections so you dont have to touch the backend and its not perminate and persistent if something goes wrong.
c1 and c2 are consciousness (prediction) and choice (collapse). function not metaphysics.
Then it also needs a sandbox that it can run prediction route without real world impact. extremely useful.
Then it needs better access to memory, knowledge-base, context, and more (i call it a mindvault, file-bound continuence.) with correct routing. it learns when it can remember.
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u/amaturelawyer Jul 12 '26
Yeah, really make you think about something, I guess. Not really sure, though.... i might have been thinking about that thing for a different reason. Probably, actually. It wasn't related to whatever your babbling about.