Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positive
Arka Pal Deep Karkhanis Samuel Dooley Manley Roberts Siddartha Naidu Colin White
Direct Preference Optimisation (DPO) is effective at significantly improving
the performance of large language models (LLMs) on downstream tasks such as
reasoning, summarisation, and alignment. Using pairs of preferred and
dispreferred data, DPO models the \textit{relative} probability of picking one
response over another. In this work, first we show theoretically that the
standard DPO loss can lead to a \textit{reduction} of the model's likelihood of
the preferred examples, as long as the relative probability between the
preferred and dispreferred classes increases. We then show empirically that
this phenomenon occurs when fine-tuning LLMs on common datasets, especially
datasets in which the edit distance between pairs of completions is low. Using
these insights, we design DPO-Positive (DPOP), a new loss function and training
procedure which avoids this failure mode. Surprisingly, we also find that DPOP
significantly outperforms DPO across a wide variety of datasets and downstream
tasks, including datasets with high edit distances between completions. By
fine-tuning with DPOP, we create and release Smaug-34B and Smaug-72B, which
achieve state-of-the-art open-source performance. Notably, Smaug-72B is nearly
2\% better than any other open-source model on the HuggingFace Open LLM
Leaderboard and becomes the first open-source LLM to surpass an average
accuracy of 80\%.