I haven't read the paper yet, looking forward to it. Using something along these lines to run a sufficiently-faithful simulation of HCH seems like a plausible path to producing an aligned AI with a halting oracle. (I don't think that even solves the problem given a halting oracle, since HCH is probably not aligned, but I still think this would be noteworthy.)

First I'm curious to understand this main result so I know what to look for and how surprised to be. In particular, I have two questions about the quantitative behavior described here:

if an event would have been unlikely had the demonstrator acted the whole time, that event's likelihood can be bounded above when running the (initially totally ignorant) imitator instead. Meanwhile, queries to the demonstrator rapidly diminish in frequency.

1: Dependence on the prior of the true hypothesis

It seems like you have the following tough case even if the human is deterministic:

• There are N hypotheses about the human, of which one is correct and the others are bad.
• I would like to make a series of N potentially-catastrophic decisions.
• On decision k, all (N-1) bad hypotheses propose the same catastrophic prediction. We don't know k in advance

Edit: I no longer endorse this comment; see this comment instead.

I think you've just assumed the entire inner alignment problem away. You assume that the model—the imitation learner—is a perfect Bayesian, rather than just some trained neural network or other function approximator (edit: I was just misinterpreting here and the training process is supposed to Bayesian rather than the model, see Rohin's comment below). The entire point of the inner alignment problem, though, is that you can't guarantee that your trained model is actually a perfect Bayesian—or anything else. In practice, all we actually generally do when we do ML is we train some neural network on some training data/environment and hope that the implicit inductive biases of our training process are such that we end up with a model doing the right thing, meaning that we can't really control what sort of model—Bayesian or anything else—that we get when we do ML, and that uncontrollability, in my eyes, is the inner alignment problem.

Thanks for sharing this work!

Here's my short summary after reading the slides and scanning the paper.

Because human demonstrator are safe (in the sense of almost never doing catastrophic actions), a model that imitates closely enough the demonstrator should be safe. The algorithm in this paper does that by keeping multiple models of the demonstrator, sampling the top models according to a parameter, and following what the sampled model does (or querying the demonstrator if the sample is "empty"). The probability that this algorithm does a very unlikely acti

Planned summary:

Since we probably can’t specify a reward function by hand, one way to get an agent that does what we want is to have it imitate a human. As long as it does this faithfully, it is as safe as the human it is imitating. However, in a train-test paradigm, the resulting agent may faithfully imitate the human on the training distribution but fail catastrophically on the test distribution. (For example, a deceptive model might imitate faithfully until it has sufficient power to take over.) One solution is to never stop training, that is, use an online learning setup where the agent is constantly learning from the demonstrator.

There are a few details to iron out. The agent needs to reduce the frequency with which it queries the demonstrator (otherwise we might as well just have the demonstrator do the work). Crucially, we need to ensure that the agent will never do something that the demonstrator wouldn’t have done, because such an action could be arbitrarily bad.

This paper proposes a solution in the paradigm where we use Bayesian updating rather than gradient descent to select our model, that is, we have a prior over possible models and then when we see a demonstrator acti

Thanks for the post and writeup, and good work! I especially appreciate the short, informal explanation of what makes this work.

Given my current understanding of the proposal, I have one worry which makes me reluctant to share your optimism about this being a solution to inner alignment:

The scheme doesn't protect us if somehow all top-n demonstrator models have correlated errors. This could happen if they are coordinating, or more prosaically if our way to approximate the posterior leads to such correlations. The picture I have in my head for the latter is... (read more)

The way to avoid outputting predictions that may have been corrupted by a mesa-optimizer is to ask for help when plausible stochastic models disagree about probabilities.

This is exactly the method used in my paper about delegative RL and also an earlier essay that doesn't assume finite MDPs or access to rewards but has stronger assumptions about the advisor (so it's essentially doing imitation learning + denoising). I pointed out the connection to mesa-optimizers (which I called "daemons") in another essay.

It is worth noting that this approach doesn't deal with non-Cartesian daemons, i.e. malign hypotheses that attack via the physical side effects of computations rather than their output. Homomorphic encryption is still the best solution I know to these.

Interesting paper! I like the focus on imitation learning, but the really new food-for-thought thing to me is the bit about dropping i.i.d. assumptions and then seeing how far you can get. I need to think more about the math in the paper before I can ask some specific questions about this i.i.d. thing.

My feelings about the post above are a bit more mixed. Claims about inner alignment always seem to generate a lot of traffic on this site. But a lot of this traffic consists of questions and clarification about what exactly counts as an inner alignment fail... (read more)

[edited to delete and replace an earlier question] Question about the paper: under equation (3) on page 4 I am reading:

The 0 on the l.h.s. means the imitator is picking the action itself instead of deferring to the demonstrator,

This confused me initially to no end, and still confuses me. Should this be:

The 0 on the l.h.s. means the imitator is picking the action $a$ itself instead of deferring to the demonstrator or picking one of the other actions???

This would seem to be more consistent with the definitions that follow, and it would seem to make more ... (read more)