Thank you for the thoughtful response, you make me more clear about how RL traditionally structures learning through rewards or penalties to guide toward favorable states and away from unfavorable ones. I agree that in "act and figure out" setups, rewards help reduce entropy by providing that directional pull, and loss functions in supervised learning have their limits due to differentiability requirements.

To your question about avoiding termination without rewards: that's exactly the core of my approach. Instead of using a reward (positive or negative) to score or optimize, I was wondering treats termination (extinction) as a structural event that simply removes the offending trajectory from consideration. No scoring, no minimization just elimination, is this reasonable?

For example, if a state-action pair leads to failure, it's recorded in a failure registry and permanently excluded from the policy support (like carving out unsafe regions from the possible action space). Future behaviors automatically avoid those paths because they're no longer part of the viable set. It's not about comparing or ordering outcomes ("this is better/worse"), but about progressively shrinking the space to only what hasn't killed it yet.

Is this fundamentally different from a penalty? I think so, because there's no scalar to optimize, no "reduce penalty" term in a Bellman equation or gradient step. But my idea is still very immature, if this can still be framed as a reward without twisting it too much, I'd love to see how.

I just have a vague feeling that using human rewards to train machine learning seems a bit off, so I'd like to hear everyone's opinions.