An LLM-in-the-loop RL Framework for Bioinformatics Feature Selection

Xinyuan Wang; Deepti Agrawal; Yanjie Fu

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arXiv · 2610.05600

An LLM-in-the-loop RL Framework for Bioinformatics Feature Selection

Xinyuan Wang·Deepti Agrawal·Yanjie Fu

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Abstract

High-dimensional bioinformatics data, characterized by a large number of features relative to the number of samples, pose major challenges such as the ``curse of dimensionality,'' leading to overfitting, high computational cost, and poor generalization. Traditional feature selection methods often suffer from limited scalability and adaptability in such domains. We propose an LLM-in-the-loop reinforcement learning (RL) framework for bioinformatics feature selection, where the RL agent formulates feature selection as a sequential decision-making task, while the large language model (LLM) enhances the process in two ways: (1) guiding exploration through domain-informed advice, and (2) providing hybrid rewards that integrate data-driven performance with knowledge-driven evaluation. The LLM also produces explanations to improve interpretability for human experts without altering the RL policy update. Experiments on diverse bioinformatics datasets show that the LLM-in-the-loop framework outperforms baselines, achieves stable performance across downstream models, and converges faster than pure RL.

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BibTeX RIS

Xinyuan Wang, Deepti Agrawal, Yanjie Fu. 2026-10-04. An LLM-in-the-loop RL Framework for Bioinformatics Feature Selection. https://arxiv.org/abs/2610.05600

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