aiqbench analysis
This paper proposes RW-DPO, a variant of Direct Preference Optimization that assigns each preference label a learned reliability weight, so mislabeled or reversed comparisons are down-weighted rather than treated as ground truth. The theory covers when flipped labels can be identified, and the language-model experiments with 10-30% flipped labels show a win rate that holds up as noise grows. For readers fine-tuning models on human or AI preference data, the practical question is whether the reported gains persist on real, messier datasets and at larger scale, since the evidence so far is largely simulation plus one fine-tuning setup.
Reliability-Weighted Direct Preference Optimization: Penalized Weights for Robust Preference Learning
arXiv:2610.41588v1 Announce Type: cross Abstract: Direct Preference Optimization (DPO) treats every reported comparison as correct, yet preference data contain reversed, tied or otherwise noisy comparisons. RW-DPO gives each comparison an unknown reliability weight $w_i\in[0,1]$ that enters the likelihood linearly -- DPO is $w\equiv1$, a reversed comparison $w_i=0$ -- and estimates the weights jointly with the policy under a minimax-concave penalty on $-\log w_i$. The penalty keeps every comparison the model agrees with at exactly $w_i=1$ and acts only on those it contradicts, through closed-form weight updates at negligible cost over DPO. We prove that recovering all flipped comparisons from a single annotation per comparison requires margins of order $\log n$. Furthermore, in a linear-index model, starting from an initial fit pointing in the correct direction, our estimator jointly consistently recovers the preference direction and weights, as well as identifies the flipped comparisons above this margin scale, even when a fixed fraction of comparisons is flipped. In simulations RW-DPO has the lowest average estimation error among DPO, noise-rate corrections and recent robust methods, with far more precise flags than recent label-correction methods. In DPO fine-tuning of a language model with 10-30% flipped labels, its win rate does not fall as the flip rate grows and exceeds that of DPO trained on clean labels, matching the strongest recent method with corrections concentrated on the flipped labels.
Abstract:Direct Preference Optimization (DPO) treats every reported comparison as correct, yet preference data contain reversed, tied or otherwise noisy comparisons. RW-DPO gives each comparison an unknown reliability weight $w_i\in[0,1]$ that enters the likelihood linearly -- DPO is $w\equiv1$, a reversed comparison $w_i=0$ -- and estimates the weights jointly with the policy under a minimax-concave penalty on $-\log w_i$. The penalty keeps every comparison the model agrees with at exactly $w_i=1$ and acts only on those it contradicts, through closed-form weight updates at negligible cost over DPO. We prove that recovering all flipped comparisons from a single annotation per comparison requires margins of order $\log n$. Furthermore, in a linear-index model, starting from an initial fit pointing in the correct direction, our estimator jointly consistently recovers the preference direction and weights, as well as identifies the flipped comparisons above this margin scale, even when a fixed fraction of comparisons is flipped. In simulations RW-DPO has the lowest average estimation error among DPO, noise-rate corrections and recent robust methods, with far more precise flags than recent label-correction methods. In DPO fine-tuning of a language model with 10-30% flipped labels, its win rate does not fall as the flip rate grows and exceeds that of DPO trained on clean labels, matching the strongest recent method with corrections concentrated on the flipped labels.
Machine Learning (stat.ML); Machine Learning (cs.LG); Methodology (stat.ME)
arXiv:2610.41588 [stat.ML]
(or arXiv:2610.41588v1 [stat.ML] for this version)
https://doi.org/10.48550/arXiv.2610.41588 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 318 words