β-DPO (Dynamic β Calibration for DPO)

Dynamically adapts the KL penalty parameter $\beta_i$ per preference pair based on the empirical reward discrepancy $\Delta R_i$ relative to batch statistics, increasing $\beta$ on noisy low-gap pairs and decreasing $\beta$ on high-confidence pairs, paired with $\beta$-guided outlier filtering.