Distinction of model-based vs. model-free

There are certain machine-learning definitions that don’t make sense no matter how many textbook definitions I see.
Model-based vs. model-free, suppose we take a system identification perspective. In this case, we’ve defined the structure of the dynamics and would like to find parameters for the simulation such that things unfold like they do in real life. The hope is doing this in an iterative data-driven manner saves us a lot of labor. However, the bias we impose on the model via. a structure, is supposed to simultaneously, help model the dynamics while staying flexible enough to be adapted. This seems fundamentally flawed to distinguish itself from model-free methods. Model-free we don’t impose any structure of the dynamics. I see it as, we don’t constrain the unfolding of dynamics. In both cases, we’re adapting parameters with data or experience, fundamentally. (Although one, there are far fewer parameters). Ultimately the problem hinges on future experiences being selected by (initial) bias which drives the “finding parameters” objective. Either bias from previous experiences, or induced by a model, etc. Which to me, boils down to a problem of exploration vs. exploitation, rather than one of methods.
What’s the correct way to think about this?

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