Which of these five AI alignment research projects ideas are no good?

I'm studying Bayesian machine learning,
    because I want to understand how to make ML systems that notice when they
    are confused
        in order to help my reader understand how to make ML systems that will
        ask the overseer for input when doing otherwise would lead to failure.
- More a study project than a research project.

I'm studying ways to improve the sample efficiency of a supervised learner,
    because I want to know how to reduce the number of calls to H in
    ‘Supervising strong learners by amplifying weak experts’
    (https://www.lesswrong.com/s/EmDuGeRw749sD3GKd/p/xKvzpodBGcPMq7TqE),
        in order to help my reader understand how we can adapt that
        proof-of-concept for solving real world tasks that require even more
        training data.
- This doesn't just mean achieving more with the samples we have. It can mean
  finding new kinds of samples that convey more information, and finding new
  ways of extracting them from the human and conveying them to the learner.

Thanks for the votes so far! The poll is still open.

By the way, I'd prefer if you only give upvotes. That's how approval voting works. If you're concerned that it would skew my total karma, feel free to balance your upvotes by voting down this comment.

I'm studying the use of a discriminator in imitation learning,
    because I want to find out how to help humans produce demonstrations that
    the agent can imitate,
        in order to help my reader understand how we might use imitation
        learning to solve the reward engineering problem.

I'm studying the effects of importance sampling on the behaviour that an RL
agent learns,
    because I want to find out whether it can lead to undesirable outcomes
        in order to help my reader understand whether importance sampling can
        solve the problem of widely varying rewards in reward engineering.

I'm studying the effects of an inconsistent comparison function on optimizing
with comparisons,
    because I want to know whether it prevents the two agents from converging on
    a desirable equilibrium quickly enough
        in order to help my reader understand whether optimizing with
        comparisons can solve the problem of inconsistency and unreliability in
        reward engineering.