A common strategy in transfer learning is few shot fine-tuning, but its success is highly dependent on the quality of samples selected as training examples. Active learning methods such as uncertainty sampling and diversity sampling can select useful samples. However, under extremely low-resource and class-imbalanced conditions, they often favor outliers rather than truly informative samples, resulting in degraded performance. In this paper, we introduce RADS (Reinforcement Adaptive Domain Sampling), a robust sample selection strategy using reinforcement learning (RL) to identify the most informative samples. Experimental evaluations on several real world clinical datasets show our sample selection strategy enhances model transferability while maintaining robust performance under extreme class imbalance compared to traditional methods.
RADS：基于强化学习的样本选择策略提升低资源与类别不平衡临床环境下的迁移学习效果 / RADS: Reinforcement Learning-Based Sample Selection Improves Transfer Learning in Low-resource and Imbalanced Clinical Settings
本文提出了一种名为RADS的智能样本选择方法，利用强化学习在医疗数据资源稀缺且类别严重不均衡的条件下，自动挑选最有价值的样本进行微调，从而显著提升模型在新任务上的迁移效果，避免了传统方法容易选到异常样本的问题。
源自 arXiv: 2604.20256
