Self-Improving Robust Preference Optimization
Authors: name: Eugene Choi, name: Arash Ahmadian, name: Matthieu Geist, name: Oilvier Pietquin, name: Mohammad Gheshlaghi Azar

Both online and offline RLHF methods such as PPO and DPO have been extremely
successful in aligning AI with human preferences. Despite their success, the
existing methods suffer from a fundamental problem that their optimal solution
is highly task-dependent (i.e., not robust to out-of-distribution (OOD) tasks).
Here we address this challenge by proposing Self-Improving Robust Preference
Optimization SRPO, a practical and mathematically principled offline RLHF
framework that is completely robust to the changes in the task. The key idea of
SRPO is to cast the problem of learning from human preferences as a
self-improvement process, which can be mathematically expressed in terms of a
min-max objective that aims at joint optimization of self-improvement policy
and the generative policy in an adversarial fashion. The solution for this
optimization problem is independent of the training task and thus it is robust
to its changes. We then show that this objective can be re-expressed in the
form of a non-adversarial offline loss which can be optimized using standard
supervised optimization techniques at scale without any need for reward model
and online inference. We show the effectiveness of SRPO in terms of AI Win-Rate
(WR) against human (GOLD) completions. In particular, when SRPO is evaluated on
the OOD XSUM dataset, it outperforms the celebrated DPO by a clear margin of
15% after 5 self-revisions, achieving WR of 90%.