I interpret Alex as making an argument such that there is not just two vs one difficulties, but an additional difficulty. From this perspective, having two will be more of an issue than one, because you have to address strictly more things.
This makes me wonder though if there is not just some sort of direction question underlying the debate here. Because if you assume the “difficulties” are only positive numbers, then if the difficulty for the direct instillation is dinstillation and the one for the grader optimization is dinstillation+devaluation , then there’s no debate that the latter is bigger than the former.
But if you allow directionality (even in one dimension), then there’s the risk that the sum leads to less difficulty in total (by having the devaluation move in the opposite direction in one dimension). That being said, these two difficulties seem strictly additive, in the sense that I don’t see (currently) how the difficulty of evaluation could partially cancel the difficulty of instillation.
Grader-optimization has the benefit that you don’t have to specify what values you care about in advance. This is a difficulty faced by value-executors but not by grader-optimizers.
Part of my point is that the machinery you need to solve evaluation-problems is also needed to solve instillation-problems because fundamentally they are shadows of the same problem, so I’d estimate d_evaluation at close to 0 in your equations after you have dealt with d_instillation.
I interpret Alex as making an argument such that there is not just two vs one difficulties, but an additional difficulty. From this perspective, having two will be more of an issue than one, because you have to address strictly more things.
This makes me wonder though if there is not just some sort of direction question underlying the debate here. Because if you assume the “difficulties” are only positive numbers, then if the difficulty for the direct instillation is dinstillation and the one for the grader optimization is dinstillation+devaluation , then there’s no debate that the latter is bigger than the former.
But if you allow directionality (even in one dimension), then there’s the risk that the sum leads to less difficulty in total (by having the devaluation move in the opposite direction in one dimension). That being said, these two difficulties seem strictly additive, in the sense that I don’t see (currently) how the difficulty of evaluation could partially cancel the difficulty of instillation.
Two responses:
Grader-optimization has the benefit that you don’t have to specify what values you care about in advance. This is a difficulty faced by value-executors but not by grader-optimizers.
Part of my point is that the machinery you need to solve evaluation-problems is also needed to solve instillation-problems because fundamentally they are shadows of the same problem, so I’d estimate d_evaluation at close to 0 in your equations after you have dealt with d_instillation.