You'd need some coupling argument to know that the problems have related difficulty, so that if A is constantly saying "I don't know" to other similar problems it counts as evidence that A can't reliably know the answer to this one. But to be clear, we don't know how to make this particular protocol go through, since we don't know how to formalise that kind of similarity assumption in a plausibly useful way. We do know a different protocol with better properties (coming soon).
I'm very much in agreement that this is a problem, and among other things blocks us from knowing how to use adversarial attack methods (and AISI teams!) from helping here. Your proposed definition feels like it might be an important part of the story but not the full story, though, since it's output only: I would unfortunately expect a decent probability of strong jailbreaks that (1) don't count as intent misalignment but (2) jump you into that kind of red attractor basin. Certainly ending up in that kind of basin could cause a catastrophe, and I would lik...
Bounding the space of controller actions more is the key bit. The (vague) claim is that if you have an argument that an empirically tested automated safety scheme is safe, in sense that you’ll know if the output is correct, you may be able to find a more constrained setup where more of the structure is human-defined and easier to analyze, and that the originally argument may port over to the constrained setup.
I’m not claiming this is always possible, though, just that it’s worth searching for. Currently the situation is that we don’t have well-developed ar...
To clarify, such a jailbreak is a real jailbreak; the claim is that it might not count as much evidence of “intentional misalignment by a model”. If we’re happy to reject all models which can be jailbroken we’ve falsified the model, but if we want to allow models which can be jailbroken but are intent aligned we have a false negative signal for alignment.
I would expect there to be a time complexity blowup if you try to drive the entropy all the way to zero, unfortunately: such things usually have a multiplier like where is the desired entropy leakage. In practice I think that would make it feasible to not leak something like a bit per sentence, and then if you have 1000 sentence you have 1000 bits. That may mean you can get a "not 1GB" guarantee, but not something smaller than that.
+1 to the quantitative story. I’ll do the stereotypical thing and add self-links: https://arxiv.org/abs/2311.14125 talks about the purest quantitative situation for debate, and https://www.alignmentforum.org/posts/DGt9mJNKcfqiesYFZ/debate-oracles-and-obfuscated-arguments-3 talks about obfuscated arguments as we start to add back non-quantitative aspects.
Thank you!
I think my intuition is that weak obfuscated arguments occur often in the sense that it’s easy to construct examples where Alice thinks for a certain amount time and produces her best possible answer so far, but where she might know that further work would uncover better answers. This shows up for any task like “find me the best X”. But then for most such examples Bob can win if he gets to spend more resources, and then we can settle things by seeing if the answer flips based on who gets more resources.
What’s happening in the primality case is th...
Unfortunately all the positives of these books come paired with a critical flaw: Caro only manages to cover two people, and hasn’t even finished the second one!
Have you found other biographers who’ve reached a similar level? Maybe the closest I’ve found was “The Last Lion” by William Manchester, but it doesn’t really compare giving how much the author fawns over Churchill.
I endorse Neel’s argument.
(Also see more explicit comment above, apologies for trying to be cute. I do think I have already presented extensive evidence here.)
I certainly do think that debate is motivated by modeling agents as being optimized to increase their reward, and debate is an attempt at writing down a less hackable reward function. But I also think RL can be sensibly described as trying to increase reward, and generally don't understand the section of the document that says it obviously is not doing that. And then if the RL algorithm is trying to increase reward, and there is a meta-learning phenomenon that cause agents to learn algorithms, then the agents will be trying to increase reward.
R...
This is a great post! Very nice to lay out the picture in more detail than LTSP and the previous LTP posts, and I like the observations about the trickiness of the n-way assumption.
I also like the "Is it time to give up?" section. Though I share your view that it's hard to get around the fundamental issue: if we imagine interpretability tools telling us what the model is thinking, and assume that some of the content that must be communicated is statistical, I don't see how that communication doesn't need some simplifying assumption to be interp...
I do mostly think this requires whitebox methods to reliably solve, so it would be less "narrow down the search space" and more "search via a different parameterisation that takes advantage of whitebox knowledge".