Thanks. This is helpful. I agree that LTP with the distilled core assumption buys us a lot, both theoretically and probably in practice too.
> The distilled core assumption seems right to me because the neural network weights are already a distilled representation of D, and we only need to compete with that representation… My main reservation is that this seems really hard… If we require competitiveness then it seems like z has to look quite a lot like the weights of a neural network
Great, agreed with all of this.
> In writing the original post I was imagining z* being much bigger than a neural network but distilled by a neural network in some way. I’ve generally moved away from that kind of perspective, partly based on the kinds of considerations in this post
I share the top-line view, but I’m not sure what issues obfuscated arguments present for large z*, other than generally pushing more difficulty onto alignment/debate. (Probably not important to respond to, just wanted to flag in case this matters elsewhere.)
> That said, I’m not sure we require OOD generalization even if we represent z via a model Mz. E.g. suppose that Mz(i) is the ith word of the intractably-large z.
I agree that Mz (= z*) does not require OOD generalization. My claim is that the amplified model using Mz involves a ML model which must generalize OOD. On D, our y-targets are PHA(y|x,Mz) where HA is an amplified human. On D*, our y-targets are similarly PHA(y∗|x∗,Mz). The key question for me is whether our y-targets on D* are good. If we use the distilled core assumption, they are—they’re exactly the predictions the human makes after updating on D. Without it, our y-targets depend on HA, which involves a ML model.
In particular, I’m assuming H^A is something like human + policy PM(y|x,Mz), where PM was optimized to imitate H on D (with z sampled), but is making predictions on D* now. Maybe the picture is that we instead run IDA from scratch on D*? E.g. for amplification, this involves ignoring the models/policies we already have, starting with the usual unaided human supervision on D* at first, and bootstrapping all the way up. I suppose this works, but then couldn’t we just have run IDA on D* without access to Mz (which itself can still access superhuman performance)?
It seems like you should either run separate models for D and D*, or jointly train the model on both D and D*, definitely you shouldn’t train on D then run on D* (and you don’t need to!).
I suppose this works, but then couldn’t we just have run IDA on D* without access to Mz (which itself can still access superhuman performance)?
The goal is to be as good as an unaligned ML system though, not just to be better than humans. And the ML system updates on D, so we need to update on D too.
It seems like you should either run separate models for D and D*, or jointly train the model on both D and D*, definitely you shouldn’t train on D then run on D* (and you don’t need to!).
Sorry yes, you’re completely right. (I previously didn’t like that there’s a model trained on Ez∼Z,D[PHA(y|x,z)] which only gets used for finding z*, but realized it’s not a big deal.)
The goal is to be as good as an unaligned ML system though, not just to be better than humans. And the ML system updates on D, so we need to update on D too.
I agree—I mean for the alternative to be running IDA on D*, using D as an auxiliary input (rather than using indirection through Mz). In other words, if we need IDA to access a large context Mz, we could also use IDA to access a large context D? Without something like the distilled core assumption, I’m not sure if there are major advantages one way or the other?
OTOH, with something like the distilled core assumption, it’s clearly better to go through Mz, because Mz is much smaller than D (I think of this as amortizing the cost of distilling D).
Even if you were taking D as input and ignoring tractability, IDA still has to decide what to do with D, and that needs to be at least as useful as what ML does with D (and needs to not introduce alignment problems in the learned model). In the post I’m kind of vague about that and just wrapping it up into the philosophical assumption that HCH is good, but really we’d want to do work to figure out what to do with D, even if we were just trying to make HCH aligned (and I think even for HCH competitiveness matters because it’s needed for HCH to be stable/aligned against internal optimization pressure).
Thanks. This is helpful. I agree that LTP with the distilled core assumption buys us a lot, both theoretically and probably in practice too.
> The distilled core assumption seems right to me because the neural network weights are already a distilled representation of D, and we only need to compete with that representation… My main reservation is that this seems really hard… If we require competitiveness then it seems like z has to look quite a lot like the weights of a neural network
Great, agreed with all of this.
> In writing the original post I was imagining z* being much bigger than a neural network but distilled by a neural network in some way. I’ve generally moved away from that kind of perspective, partly based on the kinds of considerations in this post
I share the top-line view, but I’m not sure what issues obfuscated arguments present for large z*, other than generally pushing more difficulty onto alignment/debate. (Probably not important to respond to, just wanted to flag in case this matters elsewhere.)
> That said, I’m not sure we require OOD generalization even if we represent z via a model Mz. E.g. suppose that Mz(i) is the ith word of the intractably-large z.
I agree that Mz (= z*) does not require OOD generalization. My claim is that the amplified model using Mz involves a ML model which must generalize OOD. On D, our y-targets are PHA(y|x,Mz) where HA is an amplified human. On D*, our y-targets are similarly PHA(y∗|x∗,Mz). The key question for me is whether our y-targets on D* are good. If we use the distilled core assumption, they are—they’re exactly the predictions the human makes after updating on D. Without it, our y-targets depend on HA, which involves a ML model.
In particular, I’m assuming H^A is something like human + policy PM(y|x,Mz), where PM was optimized to imitate H on D (with z sampled), but is making predictions on D* now. Maybe the picture is that we instead run IDA from scratch on D*? E.g. for amplification, this involves ignoring the models/policies we already have, starting with the usual unaided human supervision on D* at first, and bootstrapping all the way up. I suppose this works, but then couldn’t we just have run IDA on D* without access to Mz (which itself can still access superhuman performance)?
It seems like you should either run separate models for D and D*, or jointly train the model on both D and D*, definitely you shouldn’t train on D then run on D* (and you don’t need to!).
The goal is to be as good as an unaligned ML system though, not just to be better than humans. And the ML system updates on D, so we need to update on D too.
Sorry yes, you’re completely right. (I previously didn’t like that there’s a model trained on Ez∼Z,D[PHA(y|x,z)] which only gets used for finding z*, but realized it’s not a big deal.)
I agree—I mean for the alternative to be running IDA on D*, using D as an auxiliary input (rather than using indirection through Mz). In other words, if we need IDA to access a large context Mz, we could also use IDA to access a large context D? Without something like the distilled core assumption, I’m not sure if there are major advantages one way or the other?
OTOH, with something like the distilled core assumption, it’s clearly better to go through Mz, because Mz is much smaller than D (I think of this as amortizing the cost of distilling D).
Even if you were taking D as input and ignoring tractability, IDA still has to decide what to do with D, and that needs to be at least as useful as what ML does with D (and needs to not introduce alignment problems in the learned model). In the post I’m kind of vague about that and just wrapping it up into the philosophical assumption that HCH is good, but really we’d want to do work to figure out what to do with D, even if we were just trying to make HCH aligned (and I think even for HCH competitiveness matters because it’s needed for HCH to be stable/aligned against internal optimization pressure).
Okay, that makes sense (and seems compelling, though not decisive, to me). I’m happy to leave it here—thanks for the answers!