I was just imagining a fully omnicient oracle that could tell you for each action how good that action is according to your extrapolated preferences, in which case you could just explore a bit and always pick the best action according to that oracle.

OK, let’s attach this oracle to an AI. The reason this thought experiment is weird is because the goodness of an AI’s action right now cannot be evaluated independent of an expectation about what the AI will do in the future. E.g., if the AI says the word “The…”, is that a good or bad way for it to start its sentence? It’s kinda unknowable in the absence of what its later words will be.

So one thing you can do is say that the AI bumbles around and takes reversible actions, rolling them back whenever the oracle says no. And the oracle is so good that we get CEV that way. This is a coherent thought experiment, and it does indeed make inner alignment unnecessary—but only because we’ve removed all the intelligence from the so-called AI! The AI is no longer making plans, so the plans don’t need to be accurately evaluated for their goodness (which is where inner alignment problems happen).

Alternately, we could flesh out the thought experiment by saying that the AI does have a lot of intelligence and planning, and that the oracle is doing the best it can to anticipate the AI’s behavior (without reading the AI’s mind). In that case, we do have to worry about the AI having bad motivation, and tricking the oracle by doing innocuous-seeming things until it suddenly deletes the oracle subroutine out of the blue (treacherous turn). So in that version, the AI’s inner alignment is still important. (Unless we just declare that the AI’s alignment is unnecessary in the first place, because we’re going to prevent treacherous turns via option control.)

However, I think most people underestimate how many ways there are for the AI to do the right thing for the wrong reasons (namely they think it's just about deception), and I think it's not:

Yeah I mostly think this part of your comment is listing reasons that inner alignment might fail, a.k.a. reasons that goal misgeneralization / malgeneralization can happen. (Which is a fine thing to do!)

If someone thinks inner misalignment is synonymous with deception, then they’re confused. I’m not sure how such a person would have gotten that impression. If it’s a very common confusion, then that’s news to me.

Inner alignment can lead to deception. But outer alignment can lead to deception too! Any misalignment can lead to deception, regardless of whether the source of that misalignment was “outer” or “inner” or “both” or “neither”.

“Deception” is deliberate by definition—otherwise we would call it by another term, like “mistake”. That’s why it has to happen after there are misaligned motivations, right?

Overall, I think the outer-vs-inner framing has some implicit connotation that for inner alignment we just need to make it internalize the ground-truth reward

OK, so I guess I’ll put you down as a vote for the terminology “goal misgeneralization” (or “goal malgeneralization”), rather than “inner misalignment”, as you presumably find that the former makes it more immediately obvious what the concern is. Is that fair? Thanks.

I think we need to make AI have a particular utility function. We have a training distribution where we have a ground-truth reward signal, but there are many different utility functions that are compatible with the reward on the training distribution, which assign different utilities off-distribution.
You could avoid talking about utility functions by saying "the learned value function just predicts reward", and that may work while you're staying within the distribution we actually gave reward on, since there all the utility functions compatible with the ground-truth reward still agree. But once you're going off distribution, what value you assign to some worldstates/plans depends on what utility function you generalized to.

I think I fully agree with this in spirit but not in terminology!

I just don’t use the term “utility function” at all in this context. (See §9.5.2 here for a partial exception.) There’s no utility function in the code. There’s a learned value function, and it outputs whatever it outputs, and those outputs determine what plans seem good or bad to the AI, including OOD plans like treacherous turns.

I also wouldn’t say “the learned value function just predicts reward”. The learned value function starts randomly initialized, and then it’s updated by TD learning or whatever, and then it eventually winds up with some set of weights at some particular moment, which can take inputs and produce outputs. That’s the system. We can put a comment in the code that says the value function is “supposed to” predict reward, and of course that code comment will be helpful for illuminating why the TD learning update code is structured the way is etc. But that “supposed to” is just a code comment, not the code itself. Will it in fact predict reward? That’s a complicated question about algorithms. In distribution, it will probably predict reward pretty accurately; out of distribution, it probably won’t; but with various caveats on both sides.

And then if we ask questions like “what is the AI trying to do right now” or “what does the AI desire”, the answer would mainly depend on the value function.

Actually, it may be useful to distinguish two kinds of this "utility vs reward mismatch":
1. Utility/reward being insufficiently defined outside of training distribution (e.g. for what programs to run on computronium).
2. What things in the causal chain producing the reward are the things you actually care about? E.g. that the reward button is pressed, that the human thinks you did something well, that you did something according to some proxy preferences.

I’ve been lumping those together under the heading of “ambiguity in the reward signal”.

The second one would include e.g. ambiguity between “reward for button being pressed” vs “reward for human pressing the button” etc.

The first one would include e.g. ambiguity between “reward for being-helpful-variant-1” vs “reward for being-helpful-variant-2”, where the two variants are indistinguishable in-distribution but have wildly differently opinions about OOD options like brainwashing or mind-uploading.

Another way to think about it: the causal chain intuition is also an OOD issue, because it only becomes a problem when the causal chains are always intact in-distribution but they can come apart in new ways OOD.

Thanks! But I don’t think that’s a likely failure mode. I wrote about this long ago in the intro to Thoughts on safety in predictive learning.

In my view, the big problem with model-based actor-critic RL AGI, the one that I spend all my time working on, is that it tries to kill us via using its model-based RL capabilities in the way we normally expect—where the planner plans, and the actor acts, and the critic criticizes, and the world-model models the world …and the end-result is that the system makes and executes a plan to kill us. I consider that the obvious, central type of alignment failure mode for model-based RL AGI, and it remains an unsolved problem.

I think (??) you’re bringing up a different and more exotic failure mode where the world-model by itself is secretly harboring a full-fledged planning agent. I think this is unlikely to happen. One way to think about it is: if the world-model is specifically designed by the programmers to be a world-model in the context of an explicit model-based RL framework, then it will probably be designed in such a way that it’s an effective search over plausible world-models, but not an effective search over a much wider space of arbitrary computer programs that includes self-contained planning agents. See also §3 here for why a search over arbitrary computer programs would be a spectacularly inefficient way to build all that agent stuff (TD learning in the critic, roll-outs in the planner, replay, whatever) compared to what the programmers will have already explicitly built into the RL agent architecture.

So I think this kind of thing (the world-model by itself spawning a full-fledged planning agent capable of treacherous turns etc.) is unlikely to happen in the first place. And even if it happens, I think the problem is easily mitigated; see discussion in Thoughts on safety in predictive learning. (Or sorry if I’m misunderstanding.)

Thanks!

I think “inner alignment” and “outer alignment” (as I’m using the term) is a “natural breakdown” of alignment failures in the special case of model-based actor-critic RL AGI with a “behaviorist” reward function (i.e., reward that depends on the AI’s outputs, as opposed to what the AI is thinking about). As I wrote here:

Suppose there’s an intelligent designer (say, a human programmer), and they make a reward function R hoping that they will wind up with a trained AGI that’s trying to do X (where X is some idea in the programmer’s head), but they fail and the AGI is trying to do not-X instead. If R only depends on the AGI’s external behavior (as is often the case in RL these days), then we can imagine two ways that this failure happened:

1. The AGI was doing the wrong thing but got rewarded anyway (or doing the right thing but got punished)
2. The AGI was doing the right thing for the wrong reasons but got rewarded anyway (or doing the wrong thing for the right reasons but got punished).

I think it’s useful to catalog possible failures based on whether they involve (1) or (2), and I think it’s reasonable to call them “failures of outer alignment” and “failures of inner alignment” respectively, and I think when (1) is happening rarely or not at all, we can say that the reward function is doing a good job at “representing” the designer’s intention—or at any rate, it’s doing as well as we can possibly hope for from a reward function of that form. The AGI still might fail to acquire the right motivation, and there might be things we can do to help (e.g. change the training environment), but replacing R (which fires exactly to the extent that the AGI’s external behavior involves doing X) by a different external-behavior-based reward function R’ (which sometimes fires when the AGI is doing not-X, and/or sometimes doesn’t fire when the AGI is doing X) seems like it would only make things worse. So in that sense, it seems useful to talk about outer misalignment, a.k.a. situations where the reward function is failing to “represent” the AGI designer’s desired external behavior, and to treat those situations as generally bad.

(A bit more related discussion here.)

That definitely does not mean that we should be going for a solution to outer alignment and a separate unrelated solution to inner alignment, as I discussed briefly in §10.6 of that post, and TurnTrout discussed at greater length in Inner and outer alignment decompose one hard problem into two extremely hard problems. (I endorse his title, but I forget whether I 100% agreed with all the content he wrote.)

I find your comment confusing, I’m pretty sure you misunderstood me, and I’m trying to pin down how …

One thing is, I’m thinking that the AGI code will be an RL agent, vaguely in the same category as MuZero or AlphaZero or whatever, which has an obvious part of its source code labeled “reward”. For example, AlphaZero-chess has a reward of +1 for getting checkmate, -1 for getting checkmated, 0 for a draw. Atari-playing RL agents often use the in-game score as a reward function. Etc. These are explicitly parts of the code, so it’s very obvious and uncontroversial what the reward is (leaving aside self-hacking), see e.g. here where an AlphaZero clone checks whether a board is checkmate.

Another thing is, I’m obviously using “alignment” in a narrower sense than CEV (see the post—“the AGI is ‘trying’ to do what the programmer had intended for it to try to do…”)

Another thing is, if the programmer wants CEV (for the sake of argument), and somehow (!!) writes an RL reward function in Python whose output perfectly matches the extent to which the AGI’s behavior advances CEV, then I disagree that this would “make inner alignment unnecessary”. I’m not quite sure why you believe that. The idea is: actor-critic model-based RL agents of the type I’m talking about evaluate possible plans using their learned value function, not their reward function, and these two don’t have to agree. Therefore, what they’re “trying” to do would not necessarily be to advance CEV, even if the reward function were perfect.

If I’m still missing where you’re coming from, happy to keep chatting :)

In [Intro to brain-like-AGI safety] 10. The alignment problem and elsewhere, I’ve been using “outer alignment” and “inner alignment” in a model-based actor-critic RL context to refer to:

“Outer alignment” entails having a ground-truth reward function that spits out rewards that agree with what we want. “Inner alignment” is having a learned value function that estimates the value of a plan in a way that agrees with its eventual reward.

For some reason it took me until now to notice that:

• my “outer misalignment” is more-or-less synonymous with “specification gaming”,
• my “inner misalignment” is more-or-less synonymous with “goal misgeneralization”.

(I’ve been regularly using all four terms for years … I just hadn’t explicitly considered how they related to each other, I guess!)

I updated that post to note the correspondence, but also wanted to signal-boost this, in case other people missed it too.

~~

[You can stop reading here—the rest is less important]

If everybody agrees with that part, there’s a further question of “…now what?”. What terminology should I use going forward? If we have redundant terminology, should we try to settle on one?

One obvious option is that I could just stop using the terms “inner alignment” and “outer alignment” in the actor-critic RL context as above. I could even go back and edit them out of that post, in favor of “specification gaming” and “goal misgeneralization”. Or I could leave it. Or I could even advocate that other people switch in the opposite direction!

One consideration is: Pretty much everyone using the terms “inner alignment” and “outer alignment” are not using them in quite the way I am—I’m using them in the actor-critic model-based RL context, they’re almost always using them in the model-free policy optimization context (e.g. evolution) (see §10.2.2). So that’s a cause for confusion, and point in favor of my dropping those terms. On the other hand, I think people using the term “goal misgeneralization” are also almost always using them in a model-free policy optimization context. So actually, maybe that’s a wash? Either way, my usage is not a perfect match to how other people are using the terms, just pretty close in spirit. I’m usually the only one on Earth talking explicitly about actor-critic model-based RL AGI safety, so I kinda have no choice but to stretch existing terms sometimes.

Hmm, aesthetically, I think I prefer the “outer alignment” and “inner alignment” terminology that I’ve traditionally used. I think it’s a better mental picture. But in the context of current broader usage in the field … I’m not sure what’s best.

(Nate Soares dislikes the term “misgeneralization”, on the grounds that “misgeneralization” has a misleading connotation that “the AI is making a mistake by its own lights”, rather than “something is bad by the lights of the programmer”. I’ve noticed a few people trying to get the variation “goal malgeneralization” to catch on instead. That does seem like an improvement, maybe I'll start doing that too.)