preference optimization

PLC-DPO: Posterior Label Correction in Noisy and Ambiguous Preference Optimizationarxiv.org

PLC‑DPO extends Direct Preference Optimization by using the policy‑reference margin to route each training pair into clean, flipped, or tie categories, actively correcting noisy or ambiguous labels. Across extensive benchmarks it improves mean win‑rate from 55.5 % to 60.5 % and stays stable under injected noise and tie stress tests.