#openledger $OPEN

Angle: The V() interpretability score inside OpenLedger's RLHF reward function

What caught my attention reading @OpenLedger 's reinforcement learning section was a function most people scroll past entirely. V(yi, fθ(xi)) is the validator assigned score that measures not just whether a model output is correct but whether it is interpretable to a human reviewer. Both dimensions feed directly into the reward signal that shapes the next training update. Interpretability here is not a UI feature or a reporting metric it is a gradient. It changes how the model learns.

What I think this means in practice is that OpenLedger's specialized models cannot survive on accuracy alone. In healthcare, legal and finance the exact sectors this architecture targets an output that cannot be audited and explained by a domain expert is an output that cannot be used. The reward function already knows that.

#OpenLedger