Another idea would be to only use the optimized planner to write a single randomly chosen step in the CoT.
The only difference between this and my proposal is that the steps up until that randomly chosen step are written by the frozen planner, right? This variant would just make it so the optimized planner is now OOD during deployment, without any advantage AFAICT.
The frozen planner may react to steganography, in the same way that image-recognition NNs react to adversarial examples.
This is a good point!
Still, IMO, exploiting the frozen planner through adversarial inputs in a single step seems pretty unlikely to be a fruitful strategy for the optimized planner. If the optimized planner is simply trying to accurately convey information to the frozen planner, probably >99% of that information is best to convey through human-understandable text.
This is especially likely to hold under the following conditions:
If the initial paraphraser doesn't change between '!' and '.', then learning a canonical-paraphraser from that initial paraphraser won't do it either.
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So the canonical-paraphrasing idea helps somewhat, but fundamentally, the better the paraphraser (the more closely it captures the human notion of semantic equivalence), the better the anti-steganography guarantee we can get.
What I meant is that we train the canonical paraphraser based on the "human notion of semantic equivalence," NOT the "initial paraphraser."
One way of doing this is to train an...
I believe o1-type models that are trained to effectively reason out loud may actually be better for AI safety than the alternative. However, this is conditional on their CoTs being faithful, even after optimization by RL. I believe that scenario is entirely possible, though not necessarily something that will happen by default. See the case for CoT unfaithfulness is overstated, my response with specific ideas for ensuring faithfulness, and Daniel Kokotajlo's doc along similar lines.
There seems to be quite a lot of low-hanging fruit here! I'm optimistic tha...
It depends on what the alternative is. Here's a hierarchy:
1. Pretrained models with a bit of RLHF, and lots of scaffolding / bureaucracy / language model programming.
2. Pretrained models trained to do long chains of thought, but with some techniques in place (e.g. paraphraser, shoggoth-face, see some of my docs) that try to preserve faithfulness.
3. Pretrained models trained to do long chains of thought, but without those techniques, such that the CoT evolves into some sort of special hyperoptimized lingo
4. As above except with recurrent models / neuralese,...
Yep, or use method #2 on my list to make the paraphraser remove as much information as possible
Yeah, this is actually very similar to an earlier version of incremental steering, before I thought of making the "frozen planner" jump in and finish the reasoning process.
The problem is, even if we suppose that the Shoggoth won't be at all motivated to hide its reasoning from the Face (i.e. ignoring what @ryan_greenblatt brought up in his comment), the Shoggoth still might use steganography in order to make its thoughts more compact (or just because it's not particularly motivated to make everything human-understandable).
Even if we don't reward the planne...
This post seems interesting and promising, thanks for writing it!
The most predictable way zero-sum competition can fail is if one of the models is consistently better than the other at predicting.
I think this could be straightforwardly solved by not training two different models at all, but by giving two instances of the same model inputs that are both slightly perturbed in the same random way. Then, neither instance of the model would ever have a predictable advantage over the other.
For instance, in your movie recommendation example, let's say the mod...
I was talking about ELK in a group, and the working example of the SmartVault and the robber ended up being a point of confusion for us. Intuitively, it seems like the robber is an external, adversarial agent who tries to get around the SmartVault. However, what we probably care about in practice would be how a human could be fooled by an AI - not by some other adversary. Furthermore, it seems that whether the robber decides to cover up his theft of the diamond by putting up a screen depends solely on the actions of the AI. Does this imply that the ro...
The optimized planner could also manipulate the frozen planner even without using "steganography" per se. If it wants to use a line of reasoning that the frozen planner is unlikely to follow, it can substitute alternative reasoning that will lead the frozen planner to the same conclusion.
For example, suppose that the frozen planner is highly unlikely to write reasoning that shows an explicit gender bias. This could be true even if the frozen planner wasn't intentionally given harmlessness training, because most(?) people nowadays aren't explicitly sexist.
H... (read more)