D2PO: Discriminator-Guided DPO with Response Evaluation Models

Prasann Singhal, Nathan Lambert, Scott Niekum, Tanya Goyal, Greg Durrett

Research Area: Alignment, Data, Learning algorithms for LMs

Keywords: RLHF, Reward Models, Data Efficiency

TL;DR: We propose a data efficient technique based on updating discriminator models online to improve RLHF

Abstract: Varied approaches for aligning language models have been proposed, including supervised fine-tuning, RLHF, and direct optimization methods such as DPO. Although DPO has rapidly gained popularity due to its straightforward training process and competitive results, there is an open question of whether there remain practical advantages of using a discriminator, such as a reward model, to evaluate responses. We propose D2PO, discriminator-guided DPO, an approach for the online setting where preferences are being collected throughout learning. As we collect gold preferences, we use these not only to train our policy, but to train a discriminative response evaluation model to silver-label even more synthetic data for policy training. We explore this approach across a set of diverse tasks, including a realistic chat setting, and we find that our approach can lead to higher-quality outputs compared to DPO with the same data budget, and greater efficiency in terms of preference data requirements. Furthermore, we show that our silver labeling is most helpful when training the policy with DPO, outperforming traditional PPO, and benefits from maintaining a separate discriminator from the policy model.

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Submission Number: 868