I just read your Googledoc: I think your Claude instance missed the point. Yes, creating inverse karma would absolutely be a different, much more complex type of process. The joke is that this only appears true to us because we lack [Untranslatable] and somehow in the real world, using [Untranslatable] actually makes this fairly straightforward. This is impossible to prove or disprove by design. How can you argue about anything, when the premise is that your entire process for epistemic reasoning has been adversarially manipulated to be fundamentally flawed?
Sure, here is the conversation I had with Claude: https://claude.ai/share/d501d694-8764-4fb2-a1d7-b57c2cd40748
I would say that most of the philosophy and around half the jokes came from me. Claude filled in the details.
1Thank you!
I gave it the premise and asked for interesting directions to take it in. Then I told it which ones I liked the most. After a couple of iterations of ideation, I asked it to review, filter which of the scenes are the best ones, and come up with some options for the overall flow. Then I again selected the best variant. Once everything was clarified, I asked it to write the whole thing. Then I looked it over and made minor suggestions for improvement. All the final writing was by Claude. It's like being the manager of a writer, instead of writing yourself, except that some of the jokes were things I provided verbatim.
Yes! That formula is a mathematical way to express what I tried to convey in vague words. Thank you!
I'm going to get on that.
In the meantime: I have just updated the article with new results. It now includes baselines as well as a comparison with a model that was trained without any reward hacking related topics, which still performed just as well. This provides further evidence that it is generically honest and not specific to reward hacks.
1Glad to hear it! I did the same thing and will be updating the results section shortly with this and other results
Yes they are. I just wanted to point out that we tried very many different interventions and they all had similar behavior.
if having access to the unadulterated thoughts of the base model* was advantageous, the LoRA weights can just learn to not mess them up
I agree in principle, but I'm worried that the training data might not be good enough. I'm worried that any training data that slips through our quality filtering would cause more damage if we don't use lora-masking than if we do
Can we train models to be honest without any examples of dishonesty?
The intuition: We may not actually need to know ground truth labels about whether or not a model is lying in order to reduce the tendency to lie. Maybe it's enough to know the relative tendencies between two similar samples?
Outline of the approach: For any given Chain of Thought, we don't know if the model was lying or not, but maybe we can construct two variants A and B where one is more likely to be lying than the other. We then reward the one that is more honest relative to the one that is less likely to be honest.
The question: Can we construct a training method so that (1) if both COTs are lying or both are honest, the rewards will cancel out so we don't do any harm and (2) if one was lying and the other was honest then we have a good guess as to which is which because of the way we constructed the data, so we tendentially push the model towards honesty.
Crucially, we would never know if any given COT was actually honest or not, we would just produce reward signals that push towards honesty on average.
I haven't found a great mechanism yet, but I feel like the following is promising and I want to get other people's take on the idea: We generate [input-1] and [input-2], which are identical except for some small hint based on misalignment / dishonesty. We run the model on [input-1] and get [input-1][output]. We then simply pretend that we also had [input-2][output] with the same output. Because of the construction of [input-1] and [input-2], one of these two would be tendentially more aligned than the other. It's possible they are both aligned, or that neither is aligned, but one of them is more likely than the other to be aligned. So we apply an update step that rewards the difference between them.
I haven't come up with a formula yet, but I'm sure something like this has been done before.