This paper demonstrates a novel training approach which could yield narrow capability growth on a certain class of tasks.
The narrow test tube environment in which we see better performance hints at the unknown which when better understood could promise further yields down the road.
To my mind, the idea that filler tokens might promote immergent capability leading to broader task complexity capability is more promising than the backdoor risk you lay out. The possible scale in each direction just doesn't seem comparable to me(assuming each scenario plays out in a meaningful way).
Re the article...
A single fundamental breakthrough could make his entire article obsolete in a single month. We've found a lot of limits to LLMs sure... This is always how it goes over the history of AI right? The pace of fundamental breakthroughs seems of more relevant conversation with respect to the prospects for AGI as framed by his article.