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arXiv · 2509.02709

Preference Robustness for DPO with Applications to Public Health

Cheol Woo Kim·Shresth Verma·Mauricio Tec·Milind Tambe
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Abstract

We study an LLM fine-tuning task for designing reward functions for sequential resource allocation problems in public health, guided by human preferences expressed in natural language. This setting presents a challenging testbed for alignment due to complex and ambiguous objectives and limited data availability. We propose DPO-PRO, a robust fine-tuning algorithm based on Direct Preference Optimization (DPO), which accounts for uncertainty in the preference distribution using a lightweight Distributionally Robust Optimization (DRO) formulation. Unlike prior DRO-based DPO methods, DPO-PRO is significantly less conservative. We evaluate DPO-PRO on a real-world maternal mobile health program operated by the non-profit organization ARMMAN, as well as on standard alignment benchmarks. Experimental results demonstrate that our method consistently improves robustness to noisy preference signals compared to existing DPO variants. Moreover, DPO-PRO achieves comparable performance to prior self-reflection-based baseline for reward function design, while requiring significantly lower inference-time cost.

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Cheol Woo Kim, Shresth Verma, Mauricio Tec, Milind Tambe. 2025-09-02. Preference Robustness for DPO with Applications to Public Health. https://arxiv.org/abs/2509.02709

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