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This paper addresses the limitations of fixed or simplistic margin-based optimization in reward model learning from preferences within RLHF, arguing that these methods fail to account for varying preference strengths. They introduce DPO-PoP, an extension to Direct Preference Optimization (DPO), that incorporates adaptive margins inferred from preference-over-preference supervision to improve both discriminative and generative performance. Experiments on the UltraFeedback dataset demonstrate that DPO-PoP outperforms vanilla DPO and other margin-based DPO variants, while also highlighting a tradeoff between discriminative and generative performance.
Forget fixed margins in RLHF: modeling the *strength* of human preferences with "preference-over-preference" learning boosts both discriminative accuracy and generative quality.
Margin-based optimization is fundamental to improving generalization and robustness in classification tasks. In the context of reward model learning from preferences within Reinforcement Learning from Human Feedback (RLHF), existing methods typically rely on no margins, fixed margins, or margins that are simplistic functions of preference ratings. However, such formulations often fail to account for the varying strengths of different preferences, for example some preferences are associated with larger margins between responses, or they rely on noisy margin information derived from ratings. We argue that modeling the strength of preferences can lead to better generalization and more faithful alignment. Furthermore, many existing methods that use adaptive margins assume access to accurate preference scores, which can be difficult for humans to provide reliably. We propose an approach that leverages preferences over preferences, that is annotations indicating which of two preferences reflects a stronger distinction. We use this ordinal signal to infer adaptive margins on a per-datapoint basis. We introduce an extension to Direct Preference Optimization (DPO), DPO-PoP, that incorporates adaptive margins from preference-over-preference supervision, enabling improved discriminative and generative performance. Empirically, our method outperforms vanilla DPO, DPO with fixed margins, and DPO with ground-truth margins on the UltraFeedback dataset. Additionally, we show that there is a tradeoff between discriminative and generative performance: improving test classification accuracy, particularly by correctly labeling weaker preferences at the expense of stronger ones, can lead to a decline in generative quality. To navigate this tradeoff, we propose two sampling strategies to gather preference-over-preference labels: one favoring discriminative performance and one favoring generative performance.
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