Towards_Keeperhood comments on Prizes for ELK proposals

[Towards_Keeperhood]

Edit: I was wrong. You can ignore this comment thread.

I think that this:

def reporter_loss(human, θ, θ_reporter):
before, action, after = dataset.sample()
question = human.pose_question(before, action, after)
z, ε = posterior(before, action, after, θ).sample_with_noise()
answer = reporter(question, ε, θ_reporter)
loss = human.loss_for_answer(before, action, after, question, answer)
return loss + lambda * regularizer(question, ε, θ_reporter)

Should actually be this (look at ε and z):

def reporter_loss(human, θ, θ_reporter):
before, action, after = dataset.sample()
question = human.pose_question(before, action, after)
ε, z = posterior(before, action, after, θ).sample_with_noise()
answer = reporter(question, z, θ_reporter)
loss = human.loss_for_answer(before, action, after, question, answer)
return loss + lambda * regularizer(question, z, θ_reporter)

Does this resolve your confusion?

[paulfchristiano]

We want to give the reporter $\epsilon$ so that they need to reconstruct $z$ themselves (and therefore have to pay for rerunning the parts of the predictor’s model that are necessary).

[Towards_Keeperhood]

Ooops, silly me. Jumping to conclusions when I haven’t even read the text between the code.

Feel free to delete my comment as it may only cause confusion.

Also, I think you probably still want to flip z and ε in the following line:

z, ε = posterior(before, action, after, θ).sample_with_noise()