RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment
This work addresses the need for more data-efficient training paradigms in deep learning, offering a novel approach to mitigate redundancy in large-scale datasets.
The paper tackled the problem of high computational and storage costs in deep learning by developing a reinforcement learning-based method for data selection to reduce dataset redundancy, resulting in enhanced generalization performance and improved training efficiency across benchmark datasets.
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.