Offline Constrained RLHF with Multiple Preference Oracles
We study offline constrained reinforcement learning from human feedback with multiple preference oracles. Motivated by applications that trade off performance with safety or fairness, we aim to maximize target population utility subject to a minimum protected group welfare constraint. From pairwise comparisons collected under a reference policy, we estimate oracle-specific rewards via maximum likelihood and analyze how statistical uncertainty propagates through the dual program. We cast the constrained objective as a KL-regularized Lagrangian whose primal optimizer is a Gibbs policy, reducing learning to a convex dual problem. We propose a dual-only algorithm that ensures high-probability...
| Authors | Brenden Latham, Mehrdad Moharrami |
|---|---|
| Primary category | cs.LG |
| Published | 31 March 2026 |
| Last updated at source | 18 September 2026 |
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