Self-Improving Robust Preference Optimization
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%.
665fe77de659976ababbebaf
Eugene Choi
admin_assigned
2024-06-05T09:35:20.224Z
60c341c722a9d3ec68102981
/avatars/bac7c3ce9f12d0c16c63ddd31a21e71f.svg
Eugene Choi
user
eugenechoi
665fe77de659976ababbebb0
Arash Ahmadian
admin_assigned
2024-06-05T09:35:26.069Z
661b21f8ea926e8f86729e61
https://cdn-avatars.huggingface.co/v1/production/uploads/661b21f8ea926e8f86729e61/gM59piqaqjEu2IPbDvCbZ.jpeg
Arash Ahmadian
user
ArashAhmadian
665fe77de659976ababbebb1
Matthieu Geist
claimed_verified
2025-05-19T13:09:40.615Z
67bef153eaca0bef78ed5478
/avatars/ab838c541bd170986370a99670981c2f.svg
Matthieu Geist
user
matthieu-geist
665fe77de659976ababbebb2
Oilvier Pietquin
665fe77de659976ababbebb3
Mohammad Gheshlaghi Azar
Self-Improving Robust Preference Optimization
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%.
SRPO, a self-improving offline RLHF framework, achieves robustness to out-of-distribution tasks by optimizing a min-max objective that jointly enhances self-improvement and generative policies, leading to superior performance compared to DPO.
PPO DPO Self-Improving Robust Preference Optimization SRPO offline RLHF self-improvement process min-max objective self-improvement policy generative policy adversarial fashion non-adversarial offline loss supervised optimization reward model