| title | Online Learning with LLM Experts from Limited Feedback |
|---|---|
| authors | Wang Wei, Soumyabrata Pal, Koyel Mukherjee, Franck Dernoncourt, Ryan A. Rossi, Branislav Kveton, Hoda Eldardiry |
| date | 2026-09-05 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2609.05820 |
| upvotes | 6 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| 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 O(d T / m), while in the bandit setting we achieve O(d T 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. |
cite
@misc{wei2026online,
title={Online Learning with LLM Experts from Limited Feedback},
author={Wang Wei and Soumyabrata Pal and Koyel Mukherjee and Franck Dernoncourt and Ryan A. Rossi and Branislav Kveton and Hoda Eldardiry},
year={2026},
eprint={2609.05820},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2609.05820}
}