标题： RL-Selector：通过冗余评估的强化学习引导数据选择 显示英文标题

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

摘要： 现代深度架构通常依赖大规模数据集，但在这类数据集上进行训练会带来高昂的计算和存储开销。 现实世界的数据集通常包含大量冗余，这促使需要更高效的数据训练范式。 数据选择已被证明可以通过识别最具代表性的样本来减轻冗余，从而在不损害性能的情况下降低训练成本。 现有方法通常依赖静态评分指标或预训练模型，忽视了所选样本及其在训练过程中动态变化的综合影响。 我们引入了epsilon样本覆盖的概念，该概念基于样本间的关系量化样本冗余，捕捉数据集的内在结构。 基于此，我们将数据选择重新表述为强化学习（RL）过程，并提出RL-Selector，其中轻量级的RL代理通过利用从动态数据集分布中得出的epsilon样本覆盖作为奖励信号来优化选择策略。 在基准数据集和多种架构上的广泛实验表明，我们的方法始终优于现有的最先进基线。 使用我们选择的数据集训练的模型表现出增强的泛化性能并提高了训练效率。 显示英文摘要

摘要： 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.