i ran these and found that that the preference shifts aren’t due to the off-policy training (green model). I tried training on on-policy completions and in fact the effect is stronger with on-policy training
Wow I did not expect such a huge difference between on policy and off policy! Are the hyperparameters/number of datapoints all the same? How does it compare with the simple prompted version (i.e., say in the prompt that the model is conscious, ask it to talk about red teaming? Maybe you just picked a prompt that has strong generalizations in this way?
I was surprised, too, same hyperparams / number of datapoints.
Post-hoc thinking: I suspect most of the effect is from the on-policy completions having more tokens that elaborate about consciousness/emotions. The off-policy data didn’t talk about consciousness all the time. But the on-policy data talks about it much more often. So consciousness is much more salient in the data and resultant model.
Overall the simple prompted version has still has the strongest results, i think.
i ran these and found that that the preference shifts aren’t due to the off-policy training (green model). I tried training on on-policy completions and in fact the effect is stronger with on-policy training
Wow I did not expect such a huge difference between on policy and off policy! Are the hyperparameters/number of datapoints all the same? How does it compare with the simple prompted version (i.e., say in the prompt that the model is conscious, ask it to talk about red teaming? Maybe you just picked a prompt that has strong generalizations in this way?
I was surprised, too, same hyperparams / number of datapoints.
Post-hoc thinking: I suspect most of the effect is from the on-policy completions having more tokens that elaborate about consciousness/emotions. The off-policy data didn’t talk about consciousness all the time. But the on-policy data talks about it much more often. So consciousness is much more salient in the data and resultant model.
Overall the simple prompted version has still has the strongest results, i think.
on why on-policy has a big effect: probably due to the longer completions (SFT more on tokens that talk about being a conscious AI)