I haven't put much thought into this post; it's off the cuff.
DeepMind has published a couple of papers on maximizing empowerment as a form of intrinsic motivation for Unsupervised RL / Intelligent Exploration.
I never looked at either paper in detail, but the basic idea is that you should seek to maximize mutual information between (future) outcomes and actions or policies/options. Doing so means an agent knows what strategy to follow to accomplish a given outcome.
It seems plausible that instead minimizing empowerment in the case where there is a reward function could help steer an agent away from pursuing instrumental goals which have large effects.
So that might be useful for "taskification", "limited impact", etc.