A team is training a reward model using preference data. They are considering two different loss formulations for a single data sample consisting of a prompt x, a preferred response y_pref, and a rejected response y_rej.

Loss Formulation A: Loss = -log(Sigmoid(R(x, y_pref) - R(x, y_rej)))

Loss Formulation B: Loss = -Pr(y_pref ≻ y_rej | x) * log(Sigmoid(R(x, y_pref) - R(x, y_rej)))

Explain the practical implication of including the Pr(y_pref ≻ y_rej | x) term in Loss Formulation B. How does this term change the way the model learns from the preference data compared to using Loss Formulation A?