Good question. The original blog post somewhat covers this:

> Imagine a researcher who has trained an agent A_1 for some time, but now wants to experiment with better architectures or algorithms. While the tabula rasa workflow requires retraining another agent from scratch, Reincarnating RL provides the more viable option of transferring the existing agent A1 to a different agent and training this agent further, or simply fine-tuning A_1.

But this is not what happens in research. For example, each time we are training a new agent to let say play an Atari game, we train it from scratch ignoring all the prior agents trained on that game. This work argues that why not reuse learned knowledge from the existing agent while training new agents (which may be totally different).