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arXiv · 2605.14553

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits

Donghao Li·Chengshuai Shi·Weijuan Ou·Cong Shen·Jing Yang
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Abstract

Prompt engineering has become central to eliciting the capabilities of large language models (LLMs). At its core lies prompt selection -- efficiently identifying the most effective prompts. However, most prior investigations overlook a key challenge: the inherently multi-faceted nature of prompt performance, which cannot be captured by a single metric. To fill this gap, we study the multi-objective prompt selection problem under two practical settings: Pareto prompt set recovery and best feasible prompt identification. Casting the problem into the pure-exploration bandits framework, we adapt provably efficient algorithms from multi-objective bandits and further introduce a novel design for best feasible arm identification in structured bandits, with theoretical guarantees on the identification error in the linear case. Extensive experiments across multiple LLMs show that the bandit-based approaches yield significant improvements over baselines, establishing a principled and efficient framework for multi-objective prompt optimization.

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Donghao Li, Chengshuai Shi, Weijuan Ou, Cong Shen, Jing Yang. 2026-05-14. Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits. https://arxiv.org/abs/2605.14553

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