[EDIT: see my response to this comment; this one is at least mildly confused]
[Again, I want to flag that this line of thinking/disagreement is not the most interesting part of what you/Quintin are saying overall—the other stuff I intend to think more about; nonetheless, I do think it’s important to get to the bottom of the disagreement here, in case anything more interesting hinges upon it]
[JC: There isn’t an objective human reward signal that mirrors an RL agent’s reward.]
You’re the second person to confidently have this reaction, and I’m pretty confused why.
My objection here is all in the ”...that mirrors an RL agent’s reward.”—that’s where the parallel doesn’t work in my view. An RL agent is trained to maximize total (discounted) reward. The brain isn’t maximizing total reward, nor trying to maximize total reward, nor is evolution acting on the basis that it’ll do either of these things.
I agree with the following:
The brain implements an outer criterion which evaluates and reinforces behavior/predictions and incentivizes some plans over others along different dimensions.
I just don’t think this tells us anything useful, since this criterion clearly is not maximisation of total discounted reward. (though I would expect some correlation)
It seems to me that the criterion is more like maximisation of in-the-moment reward (I’m using ‘reward’ here very broadly). I.e. I might work rather than have fun since the thought of working happened to be more ‘rewarding’ than the thought of having fun. (similarly, I might not wirehead, since the thought of wireheading is negative)
This seems essentially vacuous, because I don’t see a way to measure itm-reward better than: if I did x rather than y, then x was more itm-rewarding than y. (to be clear, I’m saying this is not useful—but that I don’t see a principled definition of itm-reward that doesn’t amount to this; this is where a “crisp and clear mechanistic notion of what counted as human reward” would be handy—in order to come up with a non-vacuous definition)
Perhaps it’s clearer if I back up to your previous post and state a crisper disagreement:
If you don’t want to wirehead, you are not trying to optimize the objective encoded by the steering system in your own brain, and that’s an inner alignment failure with respect to that system.
This just seems wrong to me. The [objective encoded by the steering system] is not [maximisation of the score assigned by the steering system], but rather [whatever behaviour the steering system tends to produce].
In an RL system these two are similar, precisely because the RL system is designed to steer towards outcomes with high total discounted reward according to its own metric.
In general, steering systems are not like this. The criterion for picking one plan over another can be [expected total reward] or [something entirely different].
Where a system doesn’t use [expected total reward] it seems just plain silly to me to call behaviour misaligned where it doesn’t match [what the system would incentivize if it did use expected total reward]. Of course it doesn’t match, since that’s not how this steering system works.
Ok, putting my [maybe I’m missing the point] hat on, it strikes me that the above is considering the learned steering system—which is the outcome of any misalignment. So I probably am missing your point there (I think?). Oops.
However, I still think I’d stick to saying that:
But here I’d need to invoke properties of the original steering system (ignoring the handwaviness of what that means for now), rather than the learned steering system.
I think what matters at that point is sampling of trajectories (perhaps not only this—but at least this). There’s no mechanism in humans to sample in such a way that we’d expect maximisation of reward to be learned in the limit. Neither would we expect one, since evolution doesn’t ‘care’ about reward maximisation.
Absent such a sampling mechanism, the objective encoded isn’t likely to be maximisation of the reward.
To talk about inner misalignment, I think we need to be able to say something like:
Under [learning conditions], we expect system x to maximise y in the limit.
System x does not robustly learn to pursue y (rather than a proxy for y), so that under [different conditions] x no longer maximises y.
Here I don’t think we have (1), since we don’t expect the human system to learn to maximise reward (or minimise regret, or...) in the limit (i.e. this is not the objective encoded by their original steering system).
Anyway, hopefully it’s now clear where I’m coming from—even if I am confused!
My guess is that this doesn’t matter much to your/Quintin’s broader points(?) - beyond that “inner alignment failure” may not be the best description.