Un-learning a single training example from a trained model
is it even possible to "un-learn" a single training example without retraining on the new data?


To the best of my knowledge, the answer is no except in some very special cases.

The most obvious exception that comes to mind is instance-based learning, such as kNN: since the "model" itself consists only of the set of training instances, it's straightforward to remove an instance.

In general, supervised ML relies on generalizing patterns based on the instances from the training set. Any non-trivial model consists of multiple such patterns, with every pattern potentially resulting from a different subset of instances. Even if there was a way to trace which instance participated to which pattern (that would be extremely inefficient), removing any pattern would probably cause the model to fail.