RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment

TL;DR

RL-Selector introduces a reinforcement learning approach to data selection that dynamically assesses redundancy via epsilon-sample cover, leading to more efficient training and better generalization in deep learning models.

Contribution

We propose RL-Selector, a novel RL-based data selection method that considers evolving dataset structure and redundancy, outperforming static and pretrained model-based methods.

Findings

Outperforms state-of-the-art data selection baselines.

Reduces training data size while maintaining accuracy.

Enhances model generalization and training efficiency.

Abstract

Modern deep architectures often rely on large-scale datasets, but training on these datasets incurs high computational and storage overhead. Real-world datasets often contain substantial redundancies, prompting the need for more data-efficient training paradigms. Data selection has shown promise to mitigate redundancy by identifying the most representative samples, thereby reducing training costs without compromising performance. Existing methods typically rely on static scoring metrics or pretrained models, overlooking the combined effect of selected samples and their evolving dynamics during training. We introduce the concept of epsilon-sample cover, which quantifies sample redundancy based on inter-sample relationships, capturing the intrinsic structure of the dataset. Based on this, we reformulate data selection as a reinforcement learning (RL) process and propose RL-Selector, where a lightweight RL agent optimizes the selection policy by leveraging epsilon-sample cover derived from evolving dataset distribution as a reward signal. Extensive experiments across benchmark datasets and diverse architectures demonstrate that our method consistently outperforms existing state-of-the-art baselines. Models trained with our selected datasets show enhanced generalization performance with improved training efficiency.