Online Learning with LLM Experts from Limited Feedback

Abstract:We study adaptive routing of prompts to large language model (LLM) experts to maximize response quality in an online setting with limited feedback. We formulate it as a bandit problem with $K$ actions that represent experts and $d$ features that encode prompts, over a horizon of $T$ rounds. We propose algorithms that strategically select and observe rewards to minimize regret. In the full-information setting, we achieve a regret of $\tilde{O}(d T / \sqrt{m})$, while in the bandit setting we achieve $\tilde{O}(d T \sqrt{K / m})$, where $m \ll T$ is a budget on feedback. Our experiments show that we efficiently learn high-quality routing strategies across diverse LLMs from limited feedback.

Machine Learning (cs.LG); Machine Learning (stat.ML)

arXiv:2609.05820 [cs.LG]

(or arXiv:2609.05820v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.05820 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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