  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, like 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, we find that our approach
leads to higher-quality outputs compared to DPO with the same data budget, and
greater efficiency in terms of preference data requirements. Furthermore, we
show conditions under which silver labeling is most helpful: it is most
effective when training the policy with DPO, outperforming traditional PPO, and
benefits from maintaining a separate discriminator from the policy model.
