I feel a lot of people have already addressed the broad stretches to which people are interpreting this. Regardless, the blogpost does open interesting directions that could be useful down the line (2-5years): a) tapping into the embedding space in ways that we don't, ie editing model weights to alter task-performance (mostly preventing degradation) b) Exploring the weight space in more intuitive ways c) Understanding how we can ablate on different parts of the model to understand neuron-based interp better

Seeing this as a replacement of training seems naive, yet one can see this feedback loop targeting weights as an viable alternative to let's say doing LoRA on certain models, especially if we don't want to teach new behaviours.