Labeled TrustSet Guided: Batch Active Learning with Reinforcement Learning

2026-04-14

AI Summary

This work addresses the limitations of conventional batch active learning methods, which often fail to simultaneously account for uncertainty, diversity, and class imbalance in long-tailed distributions, while also overlooking the informative feedback from already labeled data. To overcome these challenges, the authors propose BRAL-T, a novel framework that introduces a TrustSet mechanism to curate a subset of labeled samples that are both information-rich and class-balanced. Integrated with a reinforcement learning strategy, BRAL-T efficiently selects high-quality unlabeled instances for annotation. Extensive experiments demonstrate that the proposed method consistently outperforms state-of-the-art approaches across ten image classification benchmarks and two active fine-tuning tasks, significantly enhancing both data utilization efficiency and model performance.

Technology Category

• Machine Learning: Active Learning
• Reasoning under Uncertainty: Uncertainty Representations
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Batch active learning (BAL) is a crucial technique for reducing labeling costs and improving data efficiency in training large-scale deep learning models. Traditional BAL methods often rely on metrics like Mahalanobis Distance to balance uncertainty and diversity when selecting data for annotation. However, these methods predominantly focus on the distribution of unlabeled data and fail to leverage feedback from labeled data or the model's performance. To address these limitations, we introduce TrustSet, a novel approach that selects the most informative data from the labeled dataset, ensuring a balanced class distribution to mitigate the long-tail problem. Unlike CoreSet, which focuses on maintaining the overall data distribution, TrustSet optimizes the model's performance by pruning redundant data and using label information to refine the selection process. To extend the benefits of TrustSet to the unlabeled pool, we propose a reinforcement learning (RL)-based sampling policy that approximates the selection of high-quality TrustSet candidates from the unlabeled data. Combining TrustSet and RL, we introduce the Batch Reinforcement Active Learning with TrustSet (BRAL-T) framework. BRAL-T achieves state-of-the-art results across 10 image classification benchmarks and 2 active fine-tuning tasks, demonstrating its effectiveness and efficiency in various domains.

Problem

• Batch Active Learning
• Label Efficiency
• Long-tail Problem
• Data Selection
• Model Performance Feedback

Innovation

• TrustSet
• Batch Active Learning
• Reinforcement Learning
• Label-guided Selection
• Long-tail Mitigation