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

2026-10-04

AI Summary

This study addresses the challenges of overfitting, high computational cost, and limited scalability of traditional methods in feature selection for high-dimensional bioinformatics data. To this end, we propose a large language model (LLM)-in-the-loop reinforcement learning framework that formulates feature selection as a sequential decision-making task. Specifically, the framework leverages LLMs to guide the exploration of the search space and introduces a hybrid reward mechanism integrating domain knowledge with data-driven evaluation to optimize policies. Furthermore, natural language generation techniques are incorporated to enhance interpretability. Experimental results demonstrate that the proposed approach significantly outperforms baseline methods across diverse datasets, achieving consistent improvements in downstream model performance alongside rapid convergence.

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.

Problem

• Bioinformatics
• Feature Selection
• High-dimensional Data
• Curse of Dimensionality

Innovation

• LLM-in-the-loop
• Reinforcement Learning
• Feature Selection
• Bioinformatics
• Hybrid Reward