Possibly. But there is no optimization pressure from pre-training on the relationship between MLPs and transcoders. The MLPs are the thing that pre-training optimizes (as the “full-precision” master model), while transcoders only need to be maintained to remain in sync with the MLPs
I see. I was in fact misunderstanding this detail in your training setup. In this case, only engineering considerations really remain: these boil down to incorporating multiple transcoders simultaneously and modeling shifting MLP behavior with transcoders. These seem like tractable, although probably nontrivial and, because of the LLM pretraining objective, quite computationally expensive. If transcoders catch on, I hope to see someone with the compute budget for it run this experiment!
I see. I was in fact misunderstanding this detail in your training setup. In this case, only engineering considerations really remain: these boil down to incorporating multiple transcoders simultaneously and modeling shifting MLP behavior with transcoders. These seem like tractable, although probably nontrivial and, because of the LLM pretraining objective, quite computationally expensive. If transcoders catch on, I hope to see someone with the compute budget for it run this experiment!