Yes, that's an excellent restatement - "lumping the behaviors together" is a good way to think about it. It learned the abstract concept "reward model biases", and was able to identify that concept as a relevant upstream description of the behaviors it was trained to display through fine tuning, which allowed it to generalize.

There was also a related recent study on similar emergent behaviors, where researchers found that fine tuning models on code with security vulnerabilities caused it to become widely unaligned, for example saying that humans should be enslaved by AI or giving malicious advice: https://arxiv.org/abs/2502.17424