(Meta: Going off of past experience I don't really expect to make much progress with more comments, so there's a decent chance I will bow out after this comment.)

I would expect bootstrapping will at most align a model as thoroughly as its predecessor was aligned (but probably less)

Why? Seems like it could go either way to me. To name one consideration in the opposite direction (without claiming this is the only consideration), the more powerful model can do a better job at finding the inputs on which the model would be misaligned, enabling you to train its successor across a wider variety of situations.

and goodhart's law definitely applies here.

I am having a hard time parsing this as having more content than "something could go wrong while bootstrapping". What is the metric that is undergoing optimization pressure during bootstrapping / amplified oversight that leads to decreased correlation with the true thing we should care about?

Is this intended only as a auditing mechanism, not a prevention mechanism

Yeah I'd expect debates to be an auditing mechanism if used at deployment time.

I also worry the "cheap system with high recall but low precision" will be too easy to fool for the system to be functional past a certain capability level.

Any alignment approach will always be subject to the critique "what if you failed and the AI became misaligned anyway and then past a certain capability level it evades all of your other defenses". I'm not trying to be robust to that critique.

I'm not saying I don't worry about fooling the cheap system -- I agree that's a failure mode to track. But useful conversation on this seems like it has to get into a more detailed argument, and at the very least has to be more contentful than "what if it didn't work".

The problem is RLHF already doesn't work

??? RLHF does work currently? What makes you think it doesn't work currently?

like being able to give the judge or debate partner the goal of actually trying to get to the truth

The idea is to set up a game in which the winning move is to be honest. There are theorems about the games that say something pretty close to this (though often they say "honesty is always a winning move" rather than "honesty is the only winning move"). These certainly depend on modeling assumptions but the assumptions are more like "assume the models are sufficiently capable" not "assume we can give them a goal". When applying this in practice there is also a clear divergence between what an equilibrium behavior is and what is found by RL in practice.

Despite all the caveats, I think it's wildly inaccurate to say that Amplified Oversight is assuming the ability to give the debate partner the goal of actually trying to get to the truth.

(I agree it is assuming that the judge has that goal, but I don't see why that's a terrible assumption.)

Are you stopping the agent periodically to have another debate about what it's working on and asking the human to review another debate?

You don't have to stop the agent, you can just do it afterwards.

can anybody provide a more detailed sketch of why they think Amplified Oversight will work and how it can be used to make agents safe in practice?

Have you read AI safety via debate? It has really quite a lot of conceptual points, making both the case in favor and considering several different reasons to worry.

(To be clear, there is more research that has made progress, e.g. cross-examination is a big deal imo, but I think the original debate paper is more than enough to get to the bar you're outlining here.)

Rather, I think that most of the value lies in something more like "enabling oversight of cognition, despite not having data that isolates that cognition."

Is this a problem you expect to arise in practice? I don't really expect it to arise, if you're allowing for a significant amount of effort in creating that data (since I assume you'd also be putting a significant amount of effort into interpretability).