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    <abstract>Large language models (LLMs) have recently been employed as agents to solve sequential decision-making tasks such as Bayesian optimization and multi-armed bandits. These works usually adopt an LLM for sequential action selection by providing it with a fixed, manually designed meta-prompt. However, numerous previous works have found that the prompt has a significant impact on the performance of the LLM, which calls for a method to automatically optimize the meta-prompt for LLM-based agents. Unfortunately, the non-stationarity in the reward observations during LLM-based sequential decision making makes meta-prompt optimization highly challenging. To address this challenge, we draw inspirations from adversarial bandit algorithms, which are inherently capable of handling non-stationary reward observations. Building on this foundation, we propose our EXPonential-weight algorithm for prompt Optimization (EXPO) to automatically optimize the task description and meta-instruction in the meta-prompt for LLM-based agents. We also extend EXPO to additionally optimize the exemplars (i.e., history of interactions) in the meta-prompt to further enhance the performance, hence introducing our EXPO-ES algorithm. We use extensive experiments to show that our algorithms significantly improve the performance of LLM-based sequential decision making.</abstract>
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%0 Conference Proceedings
%T Meta-Prompt Optimization for LLM-Based Sequential Decision Making
%A Kong, Mingze
%A Wang, Zhiyong
%A Shu, Yao
%A Dai, Zhongxiang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F kong-etal-2026-meta
%X Large language models (LLMs) have recently been employed as agents to solve sequential decision-making tasks such as Bayesian optimization and multi-armed bandits. These works usually adopt an LLM for sequential action selection by providing it with a fixed, manually designed meta-prompt. However, numerous previous works have found that the prompt has a significant impact on the performance of the LLM, which calls for a method to automatically optimize the meta-prompt for LLM-based agents. Unfortunately, the non-stationarity in the reward observations during LLM-based sequential decision making makes meta-prompt optimization highly challenging. To address this challenge, we draw inspirations from adversarial bandit algorithms, which are inherently capable of handling non-stationary reward observations. Building on this foundation, we propose our EXPonential-weight algorithm for prompt Optimization (EXPO) to automatically optimize the task description and meta-instruction in the meta-prompt for LLM-based agents. We also extend EXPO to additionally optimize the exemplars (i.e., history of interactions) in the meta-prompt to further enhance the performance, hence introducing our EXPO-ES algorithm. We use extensive experiments to show that our algorithms significantly improve the performance of LLM-based sequential decision making.
%U https://aclanthology.org/2026.emnlp-main.1266/
%P 28842-28867

Markdown (Informal)

[Meta-Prompt Optimization for LLM-Based Sequential Decision Making](https://aclanthology.org/2026.emnlp-main.1266/) (Kong et al., EMNLP 2026)

• Meta-Prompt Optimization for LLM-Based Sequential Decision Making (Kong et al., EMNLP 2026)

ACL

• Mingze Kong, Zhiyong Wang, Yao Shu, and Zhongxiang Dai. 2026. Meta-Prompt Optimization for LLM-Based Sequential Decision Making. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 28842–28867, Budapest, Hungary. Association for Computational Linguistics.