Why would it 2x the cost of inference? To be clear, my suggested baseline is "attach exactly the same LoRA adapters that were used for RR, plus one additional linear classification head, then train on an objective which is similar to RR but where the rerouting loss is replaced by a classification loss for the classification head." Explicitly this is to test the hypothesis that RR only worked better than HP because it was optimizing more parameters (but isn't otherwise meaningfully different from probing).

(Note that LoRA adapters can be merged into model weights for inference.)

(I agree that you could also just use more expressive probes, but I'm interested in this as a baseline for RR, not as a way to improve robustness per se.)

Thanks to the authors for the additional experiments and code, and to you for your replication and write-up!

IIUC, for RR makes use of LoRA adapters whereas HP is only a LR probe, meaning that RR is optimizing over a more expressive space. Does it seem likely to you that RR would beat an HP implementation that jointly optimizes LoRA adapters + a linear classification head (out of some layer) so that the model retains performance while also having the linear probe function as a good harmfulness classifier?

(It's been a bit since I read the paper, so sorry if I'm missing something here.)

there are multiple possible ways to interpret the final environment in the paper in terms of the analogy to the future:

1. As the catastrophic failure that results from reward hacking. In this case, we might care about frequency depending on the number of opportunities the model would have and the importance of collusion.

You're correct that I was neglecting this threat model—good point, and thanks.

So, given this overall uncertainty, it seems like we should have a much fuzzier update where higher numbers should actually update us.

Hmm, here's another way to frame this discussion:

Sam: Before evaluating the RTFs in the paper, you should apply a ~sigmoid function with the transition region centered on ~10^-6 or something. Note that all of the RTFs we're discussing come after this transition.

Ryan:  While it's true that we should first be applying a ~sigmoid, we should have uncertainty over where the transition region begins. When taking this uncertainty into account, the way to decide importance boils down to comparing the observed RTF to your distribution of thresholds in a way which is pretty similar to what people were implicitly doing anyway.

Does that sound like a reasonable summary? If so, I think I basically agree with you.

(I do think that it's important to keep in mind that for threat models where reward hacking is reinforced, small RTFs might matter a lot, but maybe that was already obvious to everyone.)

In this comment, I'll use reward tampering frequency (RTF) to refer to the proportion of the time the model reward tampers.

I think that in basically all of the discussion above, folks aren't using a correct mapping of RTF to practical importance. Reward hacking behaviors are positively reinforced once they occur in training; thus, there's a rapid transition in how worrying a given RTF is, based on when reward tampering becomes frequent enough that it's likely to appear during a production RL run.

To put this another way: imagine that this paper had trained on the final reward tampering environment the same way that it trained on the other environments. I expect that: 

• the RTF post-final-training-stage would be intuitively concerning to everyone 
• the difference between 0.01% and 0.02% RTF pre-final-training-stage wouldn't have a very big effect on RTF post-final-training-stage.

In other words, insofar as you're updating on this paper at all, I think you should be updating based on whether the RTF > 10^-6 (not sure about the exact number, but something like this) and the difference between RTFs of 1e-4 and 2e-4 just doesn't really matter.

Separately, I think it's also defensible to not update much on this paper because it's very unlikely that AI developers will be so careless as to allow models they train a chance to directly intervene on their reward mechanism. (At least, not unless some earlier thing has gone seriously wrong.) (This is my position.)

Anyway, my main point is that if you think the numbers in this paper matter at all, it seems like they should matter in the direction of being concerning. [EDIT: I mean this in the sense of "If reward tampering actually occurred with this frequency in training of production models, that would be very bad." Obviously for deciding how to update you should be comparing against your prior expectations.]

I think this is cool! The way I'm currently thinking about this is "doing the adversary generation step of latent adversarial training without the adversarial training step." Does that seem right?

It seems intuitively plausible to me that once you have a latent adversarial perturbation (the vectors you identify), you might be able to do something interesting with it beyond "train against it" (as LAT does). E.g. maybe you would like to know that your model has a backdoor, beyond wanting to move to the next step of "train away the backdoor." If I were doing this line of work, I would try to some up with toy scenarios with the property "adversarial examples are useful for reasons other than adversarial training" and show that the latent adversarial examples you can produce are more useful than input-level adversarial examples (in the same way that the LAT paper demonstrates that LAT can outperform input-level AT).

Yep, you're totally right -- thanks!

Oh, one other issue relating to this: in the paper it's claimed that if $\gamma$ is the argmin of $E[\parallel \hat{x}-{\gamma }^{\prime }x{\parallel }_2^2]$ then $1/\gamma$ is the argmin of $E[\parallel {\gamma }^{\prime }\hat{x}-x\parallel ]$. However, this is not actually true: the argmin of the latter expression is $\frac{E[x\cdot \hat{x}]}{E[\parallel \hat{x}{\parallel }_2^2]}\ne {\left(\frac{E[x\cdot \hat{x}]}{E[\parallel x{\parallel }_2]}\right)}^{-1}$. To get an intuition here, consider the case where $\hat{x}$ and $x$ are very nearly perpendicular, with the angle between them just slightly less than ${90}^{\circ }$. Then you should be able to convince yourself that the best factor to scale either $x$ or $\hat{x}$ by in order to minimize the distance to the other will be just slightly greater than 0. Thus the optimal scaling factors cannot be reciprocals of each other.

ETA: Thinking on this a bit more, this might actually reflect a general issue with the way we think about feature shrinkage; namely, that whenever there is a nonzero angle between two vectors of the same length, the best way to make either vector close to the other will be by shrinking it. I'll need to think about whether this makes me less convinced that the usual measures of feature shrinkage are capturing a real thing.

ETA2: In fact, now I'm a bit confused why your figure 6 shows no shrinkage. Based on what I wrote above in this comment, we should generally expect to see shrinkage (according to the definition given in equation (9)) whenever the autoencoder isn't perfect. I guess the answer must somehow be "equation (10) actually is a good measure of shrinkage, in fact a better measure of shrinkage than the 'corrected' version of equation (10)." That's pretty cool and surprising, because I don't really have a great intuition for what equation (10) is actually capturing.