To be clear, I haven’t explained how M− could arise nor how it’s implementing f. There are other posts that explain why gradient hacking might be a problem and informal ‘requirements’ that gradient hacking models might meet. I’m just trying to answer IF we already have a gradient hacking model, what’s the theoretical best it can do.

Can you flesh this out more by giving an example of what a training data point would be in this scenario? What are the input features of a training case and what output target variable is the model trying to predict?

Also, it is not clear from this example if the outputs of the whole composite model are:

A weighted average of the outputs of the 2 component models OR

The weighted sum of w1(M+Output)+w2(M−Output)

Output would be task-specific. In the scenario you later describe with predicting functions, you’d probably want to take option 1 (which is what parametrizing on one weight in [0,1] also accomplishes). I’ll go with that example.

But either way, I don’t see why the loss for the composite model would necessarily be equal to a weighted sum of the losses of each component model, as stated in the example above.

In principle, it seems to me the loss on such a composite model could (for example) be 0 on some data set, even if both components had a positive loss function value for that same data.

As a simplified example of this, suppose you have a 2-component weighted-average model that is trying to predict the Y value of points in a 2-dimensional (X,Y) coordinate plane given the X value. And suppose all the data points are on the line:

Y=X+1

If the 2 components of the model are:

Y=X AND

Y=X+2

The intent of the scenario is to find what model dominates, so probably loss should be non-negative. If you use squared error in that scenario, then the loss of the mixture is always greater than or equal to the loss of any particular model in the mixture.

Also suppose M− can see (w1,w2) perfectly and its loss is a function of its relative weight in the mixture. I.e. L(M−)=f(w1,w2), which I’ll refer to as M−’s loss modulating function.

I am disinclined to suppose this—unless someone explains how it could make sense for the machine learning engineers to do it that way.

My understanding is that the loss function of a model should track how close the model’s answers are to the correct or optimal answers for some data. But in this example, W1 and W2 are not part of the training data, and can vary independently from the M− outputs on the training cases.

M− has a calculable loss on the training data, which means M− could be a stand-alone model for this dataset in its own right even if M+, W1, and W2 didn’t exist.

I don’t think ML engineers will pass in weights of the models to the models themselves (except maybe for certain tasks like game-theoretic simulations). The worry is that data spills easily and that SGD might find absurd, unpredictable ways to sneak weights (or some other correlated variable) into the model. Once it does, the model’s loss is already a function of its weight (though it may of course be constant). I wouldn’t expect to run into this sort of problem with weak models operating on well-defined tasks (e.g. function prediction). Gradient hacking (unless in contrived examples) probably wouldn’t occur until we get very capable models that are fed a lot of data (the calculation of weights from which might be easy) and it may in fact never occur at all!

The result of the post is that there exists a loss-modulating function that ‘tricks’ SGD into preferring a model that is actively worse simply because the model ‘holds out’ or ‘strategically fails’ in such a way that the gradient flows toward it. How likely these functions are or if there are basins around these functions (i.e. if M− is currently outputting f′ which is sort of close to f, would SGD update M− toward f or toward some constant function? If M− is outputting f(w)=ϵ, is there any optimization pressure towards changing f to be more gradient hacky?) are open problems.

The intent of the scenario is to find what model dominates, so probably loss should be non-negative. If you use squared error in that scenario, then the loss of the mixture is always greater than or equal to the loss of any particular model in the mixture.

I don’t see why that would necessarily be true. Say you have 3 data points from my Y=X+1 example from above:

(0,1)

(1,2)

(2,3)

And say the composite model is a weighted average of Y=X and Y=X+2 with equal weights (so just the regular average).

This means that the composite model outputs will be:

For a 2-component weighted average model with a scalar output, the output should always be between between the outputs of each component model. Furthermore, if you have a such a model, and one component is getting the answers exactly correct while the other isn’t, you can always get a lower loss by giving more weight to the component model with exactly correct answers. So I would a gradient descent process to do that.

I don’t think ML engineers will pass in weights of the models to the models themselves (except maybe for certain tasks like game-theoretic simulations). The worry is that data spills easily and that SGD might find absurd, unpredictable ways to sneak weights (or some other correlated variable) into the model.

From the description, it sounded to me like this instance of gradient descent is treating the outputs of the component models M− and M+ as features in a linear regression type problem.

In such a case, I would not expect data about the weights of each model to “spill” or in any way affect the output of either component model (unless the machine learning engineers are deliberately altering the data inputs depending on what the weights are, or something like that, and I see no reason why they would do that).

If it is a different situation—like if a neural net or some part or some layers of a neural net is a “gradient hacker” I would expect under normal circumstances that gradient descent would also be optimizing the parameters within that part or those layers.

So barring some outside interference with the gradient descent process, I don’t see any concrete scenario of how gradient hacking could occur (unless the gradient hacking concept includes more mundane phenomena like “getting stuck in a local optimum”).

For a 2-component weighted average model with a scalar output, the output should always be between between the outputs of each component model.

Hm, I see your point. I retract my earlier claim. This model wouldn’t apply to that task. I’m struggling to generate a concrete example where loss would actually be a linear combination of the sub-models’ loss. However, I (tentatively) conjecture that in large networks trained on complex tasks, loss can be roughly approximated as a linear combination of the losses of subnetworks (with the caveats of weird correlations and tasks where partial combinations work well (like the function approximation above)).

I would expect under normal circumstances that gradient descent would also be optimizing the parameters within that part or those layers.

I agree, but the question of in what direction SGD changes the model (i.e. how it changes f) seems to have some recursive element analogous to the situation above. If the model is really close to the f above, then I would imagine there’s some optimization pressure to update it towards f. That’s just a hunch, though. I don’t know how close it would have to be.

To be clear, I haven’t explained how M− could arise nor how it’s implementing f. There are other posts that explain why gradient hacking might be a problem and informal ‘requirements’ that gradient hacking models might meet. I’m just trying to answer

IFwe already have a gradient hacking model, what’s the theoretical best it can do.Output would be task-specific. In the scenario you later describe with predicting functions, you’d probably want to take option 1 (which is what parametrizing on one weight in [0,1] also accomplishes). I’ll go with that example.

The intent of the scenario is to find what model dominates, so probably loss should be non-negative. If you use squared error in that scenario, then the loss of the mixture is always greater than or equal to the loss of any particular model in the mixture.

I don’t think ML engineers will pass in weights of the models to the models themselves (except maybe for certain tasks like game-theoretic simulations). The worry is that data spills easily and that SGD might find absurd, unpredictable ways to sneak weights (or some other correlated variable) into the model. Once it does, the model’s loss is already a function of its weight (though it may of course be constant). I wouldn’t expect to run into this sort of problem with weak models operating on well-defined tasks (e.g. function prediction). Gradient hacking (unless in contrived examples) probably wouldn’t occur until we get very capable models that are fed a lot of data (the calculation of weights from which might be easy) and it may in fact never occur at all!

The result of the post is that there exists

aloss-modulating function that ‘tricks’ SGD into preferring a model that is actively worse simply because the model ‘holds out’ or ‘strategically fails’ in such a way that the gradient flows toward it. How likely these functions are or if there are basins around these functions (i.e. if M− is currently outputting f′ which is sort of close to f, would SGD update M− toward f or toward some constant function? If M− is outputting f(w)=ϵ, is there any optimization pressure towards changing f to be more gradient hacky?) are open problems.I don’t see why that would necessarily be true. Say you have 3 data points from my Y=X+1 example from above:

(0,1)

(1,2)

(2,3)

And say the composite model is a weighted average of Y=X and Y=X+2 with equal weights (so just the regular average).

This means that the composite model outputs will be:

Y=(FirstComponentOutput)+(SecondComponentOutput)2=X+(X+2)2=2X+22=X+1

Thus the composite model would be right on the line, and get each data point Y-value exactly right (and have 0 loss).

The squared error loss would be:

TotalLoss=(ModelOutput(0)−1)2+(ModelOutput(1)−2)2+(ModelOutput(2)−3)2

=((0+1)−1)2+((1+1)−2)2+((2+1)−3)2=0

By contrast, each of the two component models would have a total squared error of 3 for these 3 data points.

The Y=X component model would have total squared error loss of:

TotalLoss=(ModelOutput(0)−1)2+(ModelOutput(1)−2)2+(ModelOutput(2)−3)2

=(0−1)2+(1−2)2+(2−3)2=3

The Y=X + 2 component model would have total squared error loss of:

TotalLoss=(ModelOutput(0)−1)2+(ModelOutput(1)−2)2+(ModelOutput(2)−3)2

=((0+2)−1)2+((1+2)−2)2+((2+2)−3)2=3

For a 2-component weighted average model with a scalar output, the output should always be between between the outputs of each component model. Furthermore, if you have a such a model, and one component is getting the answers exactly correct while the other isn’t, you can always get a lower loss by giving more weight to the component model with exactly correct answers. So I would a gradient descent process to do that.

From the description, it sounded to me like this instance of gradient descent is treating the outputs of the component models M− and M+ as features in a linear regression type problem.

In such a case, I would not expect data about the weights of each model to “spill” or in any way affect the output of either component model (unless the machine learning engineers are deliberately altering the data inputs depending on what the weights are, or something like that, and I see no reason why they would do that).

If it is a different situation—like if a neural net or some part or some layers of a neural net is a “gradient hacker” I would expect under normal circumstances that gradient descent would also be optimizing the parameters within that part or those layers.

So barring some outside interference with the gradient descent process, I don’t see any concrete scenario of how gradient hacking could occur (unless the gradient hacking concept includes more mundane phenomena like “getting stuck in a local optimum”).

Hm, I see your point. I retract my earlier claim. This model wouldn’t apply to that task. I’m struggling to generate a concrete example where loss would

actuallybe a linear combination of the sub-models’ loss. However, I (tentatively) conjecture that in large networks trained on complex tasks, loss can be roughly approximated as a linear combination of the losses of subnetworks (with the caveats of weird correlations and tasks where partial combinations work well (like the function approximation above)).I agree, but the question of in what direction SGD changes the model (i.e. how it changes f) seems to have some recursive element analogous to the situation above. If the model is really close to the f above, then I would imagine there’s

someoptimization pressure to update it towards f. That’s just a hunch, though. I don’t know how close it would have to be.