I think it's worth disentangling LLMs and Transformers and so on in discussions like this one--they are not one and the same. For instance, the following are distinct positions that have quite different implications:

• The current precise transformer LM setup but bigger will never achieve AGI
• A transformer trained on the language modelling objective will never achieve AGI (but a transformer network trained with other modalities or objectives or whatever will)
• A language model with the transformer architecture will never achieve AGI (but a language model with some other architecture or training process will)

Which interventions make sense depends a lot on your precise model of why current models are not AGI, and I would consequently expect modelling things at the level of "LLMs vs not LLMs" to be less effective.

Doesn't answer your question, but we also came across this effect in the RM Goodharting work, though instead of figuring out the details we only proved that it when it's definitely not heavy tailed it's monotonic, for Regressional Goodhart (https://arxiv.org/pdf/2210.10760.pdf#page=17). Jacob probably has more detailed takes on this than me. 

In any event my intuition is this seems unlikely to be the main reason for overoptimization - I think it's much more likely that it's Extremal Goodhart or some other thing where the noise is not independent

Pointing at some of the same things: https://www.lesswrong.com/posts/ktJ9rCsotdqEoBtof/asot-some-thoughts-on-human-abstractions

Adding $200 to the pool. Also, I endorse the existence of more bounties/contests like this.

re:1, yeah that seems plausible, I'm thinking in the limit of really superhuman systems here and specifically pushing back against a claim that this human abstractions being somehow inside a superhuman AI is sufficient for things to go well.

re:2, one thing is that there are ways of drifting that we would endorse using our meta-ethics, and ways that we wouldn't endorse. More broadly, the thing I'm focusing on in this post is not really about drift over time or self improvement; in the setup I'm describing, the thing that goes wrong is it does the classical "fill the universe with pictures of smiling humans" kind of outer alignment failure case (or worse yet, the more likely outcome of trying to build an agentic AGI is we fail to retarget the search and end up with one that actually cares about microscopic squiggles, and then it does the deceptive alignment using those helpful human concepts it has lying around).

one man's modus tollens is another man's modus ponens:

"making progress without empirical feedback loops is really hard, so we should get feedback loops where possible" "in some cases (i.e close to x-risk), building feedback loops is not possible, so we need to figure out how to make progress without empirical feedback loops. this is (part of) why alignment is hard"

Therefore, the longer you interact with the LLM, eventually the LLM will have collapsed into a waluigi. All the LLM needs is a single line of dialogue to trigger the collapse.

This seems wrong. I think the mistake you're making is when you argue that because there's some chance X happens at each step and X is an absorbing state, therefore you have to end up at X eventually. However, this is only true if you assume the conclusion and claim that the prior probability of luigis is zero. If there is some prior probability of a luigi, each non-waluigi step increases the probability of never observing a transition to a waluigi a little bit.