Abstract
Reinforcement Learning from Human Feedback (RLHF) produces powerful instruction-following models but relies on a preference-labeling process that is both costly and slow. An effective alternative, Reinforcement Learning from AI Feedback (RLAIF), uses large language models as teachers for relabeling; however, this introduces substantial label noise. In our setting, we found that AI teachers flipped approximately 50% of the original human preferences on the dataset, a condition that degrades the performance of standard direct preference optimization (DPO). We propose noise-robust DPO (nrDPO) and nrDPO-gated, two drop-in variants that make DPO resilient to noisy preferences. nrDPO reweights each pair by (i) a margin-confidence term from a frozen reference policy (base or SFT), (ii) a context-stability term that penalizes preferences that change under truncated histories, and (iii) a length correction to curb verbosity bias. nrDPO-gated further filters low-confidence pairs via a simple threshold on the reference margin. On a dataset with heavy synthetic noise (30% flips), nrDPO-gated improves the preference accuracy by +3.8% over vanilla DPO; in a realistic RLAIF setting, nrDPO-gated is the only configuration that recovers competitive alignment, reaching ≈60% on a 5k relabeled set (vs. ≈49–50% for vanilla DPO) and approaching RLHF baselines.