I'm not very convinced by this comment as an objection to "50% AI grabs power to get reward." (I find it more plausible as an objection to "AI will definitely grab power to get reward.")

I expect "reward" to be a hard goal to learn, because it's a pretty abstract concept and not closely related to the direct observations that policies are going to receive

"Reward" is not a very natural concept

This seems to be most of your position but I'm skeptical (and it's kind of just asserted without argument):

• The data used in training is literally the only thing that AI systems observe, and prima facie reward just seems like another kind of data that plays a similarly central role. Maybe your "unnaturalness" abstraction can make finer-grained distinctions than that, but I don't think I buy it.
• If people train their AI with RLDT then the AI is literally be trained to predict reward! I don't see how this is remote, and I'm not clear if your position is that e.g. the value function will be bad at predicting reward because it is an "unnatural" target for supervised learning.
• I don't understand the analogy with humans. It sounds like you are saying "an AI system selected based on the reward of its actions learns to select actions it expects to lead to high reward" be analogous to "humans care about the details of their reward circuitry." But:
  • I don't think human learning is just RL based on the reward circuit; I think this is at least a contrarian position and it seems unworkable to me as an explanation of human behavior.
  • It seems like the analogous conclusion for RL systems would be "they may not care about the rewards that go into the SGD update, they may instead care about the rewards that get entered into the dataset, or even something further causally upstream of that as long as it's very well-correlated on the training set." But it doesn't matter what we choose that's causally upstream of rewards, as long as it's perfectly correlated on the training set?
  • (Or you could be saying that humans are motivated by pleasure and pain but not the entire suite of things that are upstream of reward? But that doesn't seem right to me.)

[The concept of "reward"] doesn't apply outside training

I don't buy it:

• If people train AI systems on random samples of deployment, then "reward" does make sense---it's just what would happen if you sampled this episode to train on. This is done in practice today and seems like a pretty good idea (since the thing you care about is precisely the performance on deployment episodes) unless you are specifically avoiding it for safety reasons.
• It's plausible that such training is happening whether or not it actually is, and so that's a very natural objective for a system that cares about maximizing reward conditioned on an episode being selected for training.
• Even if test episodes are obviously not being used in training, there are still lots of plausible-sounding generalizations of "reward" to those episodes (whether based on physical implementation, or on the selection implemented by SGD, or based on conditioning on unlikely events, or based on causal counterfactuals...) and as far as I can tell pretty much all of them lead to the same bottom line.
• If the deployment distribution is sufficiently different from the training distribution that the AI no longer does something with the same upshot as maximizing reward, then it seems very likely that the resulting behaviors are worse (e.g. they would receive a lower score when evaluated by humans) and so people will be particularly likely to train on those deployment episodes in order to correct the issue.

So I think that if a system was strategically optimizing reward on the training set, it would probably either do something similar-enough-to-result-in-grabbing-power on the test set, or else it would behave badly and be corrected.

(Overall I find the other point more persuasive, though again I think it's better as an objection to 100% than as an objection to 50%: actually optimizing reward doesn't do much better than various soups of heuristics, and so we don't have a strong prediction that SGD will prefer one to the other without getting into the weeds and making very uncertain claims or else taking the limit.)

As one example: it means that every time you deploy the policy without the intention of rewarding it, then its key priority would be convincing you to inserting that trajectory into the training data. (It might be instructive to think about what the rewards would need to be for that not to happen. Below 0? But the 0 point is arbitrary...) That seems pretty noticeable!

RL agents receive a loss that is based on reward in training episodes. If they tried to change the probability that a given episode appeared in training, and sacrificed reward to do so, that behavior would be discouraged by SGD---it would select away from parameters that do that (since they take worse actions on the episodes that actually appear in training). So I don't think there's any reason to think that an RL agent would behave this way.

Instead, you should expect an RL agent to maximize reward conditioned on the episode appearing in training, because that's what SGD would select for. I agree that we shouldn't expect a general thematic connection to "reward" beyond what you'd expect from the mechanics of SGD.

and even within training it's dependent on the specific training algorithm you use

This is true, but as far as I can see all the possible versions result in grabbing power, so I don't think it undermines the case. I don't know if you have particular versions in mind that wouldn't result in either grabbing power or else bad behavior that would be corrected by further training.

They focus on policies learning the goal of getting high reward.

The claim is that AI systems will take actions that get them a lot of reward. This doesn't have to be based on the mechanistic claim that they are thinking about reward, it can also just be the empirical observation: somehow you selected a policy that creatively gets a lot of reward across a very broad training distribution, so in a novel situation "gets a lot of reward" is one of our most robust predictions about the behavior of the AI.

This is how Ajeya's post goes, and it explicitly notes that the AI trained with RLDT could be optimizing something else such that they only want to get reward during training, but that this mostly just makes things worse: everything seems to lead to the same place for more or less the same reason.

I think the "soup of heuristics" stories (where the AI is optimizing something far causally upstream of reward instead of something that is downstream or close enough to be robustly correlated) don't lead to takeover in the same way, and so that part of the objection is much stronger (though see my objections above).

Overall I'm concerned that reasoning about "the goal of getting high reward" is too anthropomorphic and is a bad way to present the argument to ML researchers in particular.

I don't feel like this argument is particularly anthropomorphic. The argument is that a policy selected for achieving X will achieve X in a new situation (which we can run for lots of different X, since there are many X that are perfectly correlated on the training set). In all the discussions I've been in (both with safety people and with ML people) the counterargument leans much more heavily on the analogy with humans (though I think that analogy is typically misapplied).