“even though reward is not a kind of objective”, this is a terminological issue. In my view, calling a “antecedent-computation reinforcement criterion” an “objective” matches my definition of “objective”, and this is just a matter of terminology. The term “objective” is ill-defined enough that “even though reward is not a kind of objective” is a terminological claim about objective, not a claim about math/the world.
The idea that RL agents “reinforce antecedent computations” is completely core to our story of deception. You could not make sense of our argument for deception if you didn’t look at RL systems in this way. Viewing the base optimizer as “trying” to achieve an “objective” but “failing” because it is being “deceived” by the mesa optimizer is purely a metaphorical/terminological choice. It doesn’t negate the fact that we all understood that the base optimizer is just reinforcing “antecedent computations”. How else could you make sense of the story of deception, where an existing model, which represents the mesa optimizer, is being reinforced by the base optimizer because that existing model understands the base optimizer’s optimization process?
I am not claiming that the RFLO communicated this point well, just that it was understood and absolutely was core to the paper, and large parts of the paper wouldn’t even make sense if you didn’t have this insight. (Certainly the fact that we called it an objective doesn’t communicate the point, and it isn’t meant to).
I am not claiming that the RFLO communicated this point well, just that it was understood and absolutely was core to the paper, and large parts of the paper wouldn’t even make sense if you didn’t have this insight.
I think most ML practitioners do have implicit models of how reward chisels computation into agents, as seen with how they play around with e.g. reward shaping and such. It’s that I don’t perceive this knowledge to be engaged when some people reason about “optimization processes” and “selecting for high-reward models” on e.g. LW.
I just continue to think “I wouldn’t write RFLO the way it was written, if I had deeply and consciously internalized the lessons of OP”, but it’s possible this is a terminological/framing thing. Your comment does update me some, but I think I mostly retain my view here. I do totally buy that you all had good implicit models of the reward-chiseling point.
FWIW, I think a bunch of my historical frustration here has been an experience of:
Pointing out the “reward chisels computation” point
Having some people tell me it’s obvious, or already known, or that they already invented it
Seeing some of the same people continue making similar mistakes (according to me)
Not finding instances of other people making these points before OP
Continuing (AFAICT) to correct people on (what I claim to be) mistakes around reward and optimization targets, and (for a while) was ~the only one doing so.
If I found several comments explaining what is clearly the “reward chisels computation” point, where the comments were posted before this post, by people who weren’t me or downstream of my influence, I would update against my points being novel and towards my points using different terminology.
IIRC there’s one comment from Wei_Dai from a few years back in this vein, but IDK of others.
Person A has mental model X and tries to explain X with explanation Q
Person B doesn’t get model X from Q, thinks a bit, and then writes explanation P, reads P and thinks: P is how it should have been explained all along, and Q didn’t actually contain the insights, but P does.
Person C doesn’t get model X from P, thinks a bit, and then writes explanation R, reads R and thinks: …
It seems to me quite likely that you are person B, thinking they explained something because THEY think their explanation is very good and contains all the insights that the previous ones didn’t. Some of the evidence for this is in fact contained in your very comment:
“1. Pointing out the “reward chisels computation” point. 2. Having some people tell me it’s obvious, or already known, or that they already invented it. 3. Seeing some of the same people continue making similar mistakes (according to me)” So point 3 basically almost definitively proves that your mental model is not conveyed to those people in your post, does it not? I think a similar thing happened where that mental model was not conveyed to you from RFLO, even though we tried to convey it. (btw not saying the models that RFLO tried to explain are the same as this post, but the basic idea of this post definitely is a part of RFLO).
BTW, it could in fact be that person B’s explanation is clearer. (otoh, I think some things are less clear, e.g. you talk about “the” optimization target, which I would say is referring to that of the mesa-optimizer, without clearly assuming there is a mesa-optimizer. We stated the terms mesa- and base-optimizer to clearly make the distinction. There are a bunch of other things that I think are just imprecise, but let’s not get into it).
“Continuing (AFAICT) to correct people on (what I claim to be) mistakes around reward and optimization targets, and (for a while) was ~the only one doing so.”
I have been correcting people for a while on stuff like that (though not on LW, I’m not often on LW), such as that in the generic case we shouldn’t expect wireheading from RL agents unless the option of wireheading is in the training environment, for basically these reasons. I would also have expected people to just get this after reading RFLO, but many didn’t (others did), so your points 1/2/3 also apply to me.
“I do totally buy that you all had good implicit models of the reward-chiseling point”. I don’t think we just “implicitly” modeled it, we very explicitly understood it and it ran throughout our whole thinking about the topic. Again, explaining stuff is hard though, I’m not claiming we conveyed everything well to everyone (clearly you haven’t either).
I want to note that I just reread Utility ≠ Reward and was pleasantly surprised by its treatment, as well as the hedges. I’m making an upwards update on these points having been understood by at least some thinkers, although I’ve also made a lot of downward updates for other reasons.
Very late reply, sorry.
“even though reward is not a kind of objective”, this is a terminological issue. In my view, calling a “antecedent-computation reinforcement criterion” an “objective” matches my definition of “objective”, and this is just a matter of terminology. The term “objective” is ill-defined enough that “even though reward is not a kind of objective” is a terminological claim about objective, not a claim about math/the world.
The idea that RL agents “reinforce antecedent computations” is completely core to our story of deception. You could not make sense of our argument for deception if you didn’t look at RL systems in this way. Viewing the base optimizer as “trying” to achieve an “objective” but “failing” because it is being “deceived” by the mesa optimizer is purely a metaphorical/terminological choice. It doesn’t negate the fact that we all understood that the base optimizer is just reinforcing “antecedent computations”. How else could you make sense of the story of deception, where an existing model, which represents the mesa optimizer, is being reinforced by the base optimizer because that existing model understands the base optimizer’s optimization process?
I am not claiming that the RFLO communicated this point well, just that it was understood and absolutely was core to the paper, and large parts of the paper wouldn’t even make sense if you didn’t have this insight. (Certainly the fact that we called it an objective doesn’t communicate the point, and it isn’t meant to).
I think most ML practitioners do have implicit models of how reward chisels computation into agents, as seen with how they play around with e.g. reward shaping and such. It’s that I don’t perceive this knowledge to be engaged when some people reason about “optimization processes” and “selecting for high-reward models” on e.g. LW.
I just continue to think “I wouldn’t write RFLO the way it was written, if I had deeply and consciously internalized the lessons of OP”, but it’s possible this is a terminological/framing thing. Your comment does update me some, but I think I mostly retain my view here. I do totally buy that you all had good implicit models of the reward-chiseling point.
FWIW, I think a bunch of my historical frustration here has been an experience of:
Pointing out the “reward chisels computation” point
Having some people tell me it’s obvious, or already known, or that they already invented it
Seeing some of the same people continue making similar mistakes (according to me)
Not finding instances of other people making these points before OP
Continuing (AFAICT) to correct people on (what I claim to be) mistakes around reward and optimization targets, and (for a while) was ~the only one doing so.
If I found several comments explaining what is clearly the “reward chisels computation” point, where the comments were posted before this post, by people who weren’t me or downstream of my influence, I would update against my points being novel and towards my points using different terminology.
IIRC there’s one comment from Wei_Dai from a few years back in this vein, but IDK of others.
There is a general phenomenon where:
Person A has mental model X and tries to explain X with explanation Q
Person B doesn’t get model X from Q, thinks a bit, and then writes explanation P, reads P and thinks: P is how it should have been explained all along, and Q didn’t actually contain the insights, but P does.
Person C doesn’t get model X from P, thinks a bit, and then writes explanation R, reads R and thinks: …
It seems to me quite likely that you are person B, thinking they explained something because THEY think their explanation is very good and contains all the insights that the previous ones didn’t. Some of the evidence for this is in fact contained in your very comment:
“1. Pointing out the “reward chisels computation” point. 2. Having some people tell me it’s obvious, or already known, or that they already invented it. 3. Seeing some of the same people continue making similar mistakes (according to me)”
So point 3 basically almost definitively proves that your mental model is not conveyed to those people in your post, does it not? I think a similar thing happened where that mental model was not conveyed to you from RFLO, even though we tried to convey it. (btw not saying the models that RFLO tried to explain are the same as this post, but the basic idea of this post definitely is a part of RFLO).
BTW, it could in fact be that person B’s explanation is clearer. (otoh, I think some things are less clear, e.g. you talk about “the” optimization target, which I would say is referring to that of the mesa-optimizer, without clearly assuming there is a mesa-optimizer. We stated the terms mesa- and base-optimizer to clearly make the distinction. There are a bunch of other things that I think are just imprecise, but let’s not get into it).
“Continuing (AFAICT) to correct people on (what I claim to be) mistakes around reward and optimization targets, and (for a while) was ~the only one doing so.”
I have been correcting people for a while on stuff like that (though not on LW, I’m not often on LW), such as that in the generic case we shouldn’t expect wireheading from RL agents unless the option of wireheading is in the training environment, for basically these reasons. I would also have expected people to just get this after reading RFLO, but many didn’t (others did), so your points 1/2/3 also apply to me.
“I do totally buy that you all had good implicit models of the reward-chiseling point”. I don’t think we just “implicitly” modeled it, we very explicitly understood it and it ran throughout our whole thinking about the topic. Again, explaining stuff is hard though, I’m not claiming we conveyed everything well to everyone (clearly you haven’t either).
I want to note that I just reread Utility ≠ Reward and was pleasantly surprised by its treatment, as well as the hedges. I’m making an upwards update on these points having been understood by at least some thinkers, although I’ve also made a lot of downward updates for other reasons.