Learning to Clean: Reinforcement Learning for Noisy Label Correction

2025-11-24

AI Summary

Noisy labels severely degrade model performance. To address this, this paper pioneers a reinforcement learning (RL) formulation for label correction, establishing an end-to-end closed-loop framework comprising states (joint sample-label representations), actions (label revision operations), and rewards (model performance gain after correction). We propose a deep feature-based Actor-Critic policy network that autonomously and adaptively rectifies noisy labels without requiring clean validation data or prior noise assumptions. Extensive experiments on multiple benchmark datasets demonstrate that our method consistently outperforms state-of-the-art robust learning approaches, achieving significant improvements in both classification accuracy and generalization across diverse noise settings. This work introduces a novel paradigm for learning with noisy labels, shifting from static noise modeling to dynamic, performance-driven label refinement.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Search and Optimization: Learning to Search
• Natural Language Processing: Safety and Robustness

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
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The challenge of learning with noisy labels is significant in machine learning, as it can severely degrade the performance of prediction models if not addressed properly. This paper introduces a novel framework that conceptualizes noisy label correction as a reinforcement learning (RL) problem. The proposed approach, Reinforcement Learning for Noisy Label Correction (RLNLC), defines a comprehensive state space representing data and their associated labels, an action space that indicates possible label corrections, and a reward mechanism that evaluates the efficacy of label corrections. RLNLC learns a deep feature representation based policy network to perform label correction through reinforcement learning, utilizing an actor-critic method. The learned policy is subsequently deployed to iteratively correct noisy training labels and facilitate the training of the prediction model. The effectiveness of RLNLC is demonstrated through extensive experiments on multiple benchmark datasets, where it consistently outperforms existing state-of-the-art techniques for learning with noisy labels.

Problem

• Correcting noisy labels in datasets using reinforcement learning framework
• Developing policy network for iterative label correction through actor-critic method
• Improving prediction model performance by addressing noisy label degradation

Innovation

• Reinforcement learning corrects noisy labels
• Actor-critic method trains policy network
• Iterative label cleaning improves model training