It’s not clear what the ratio of capabilities/alignment progress is for interpretability. There is not empirical track record[^1] of interpretability feeding back into improvements of any kind.
A priori it seems like it would be good because understanding how things work is useful to understand their behavior better, and thus be able to tell whether or not a model is aligned or how to make it more so. But understanding how things work is also useful for making them more capable, e.g. if you use interpretability as a model-debugger, it’s basically general purpose for dealing with ML models.
It’s not clear what the ratio of capabilities/alignment progress is for interpretability. There is not empirical track record[^1] of interpretability feeding back into improvements of any kind.
A priori it seems like it would be good because understanding how things work is useful to understand their behavior better, and thus be able to tell whether or not a model is aligned or how to make it more so. But understanding how things work is also useful for making them more capable, e.g. if you use interpretability as a model-debugger, it’s basically general purpose for dealing with ML models.
[1]: known to the author