Reinforced interactive continual learning via real-time noisy human feedback

This paper introduces an interactive continual learning paradigm in which AI models dynamically learn new skills from real-time human feedback while retaining prior knowledge. This paradigm addresses two key limitations of traditional continual learning: (1) it enables dynamic model updates using streaming, real-time human-annotated data, rather than static datasets with fixed labels, and (2) it relaxes the assumption of clean labels by explicitly handling the noisy feedback common in real-world interactions. To tackle these challenges, we propose RiCL, a Reinforced interactive Continual Learning framework that leverages Large Language Models (LLMs) to learn new skills effectively from dynamic feedback. RiCL incorporates three key components: a temporal consistency-aware purifier to automatically discern clean samples from noisy ones in data streams; an interaction-aware direct preference optimization strategy to align model behavior with human intent by reconciling AI-generated and human-provided feedback; and a noise-resistant contrastive learning module that captures robust representations by exploiting inherent data relationships, thereby reducing reliance on potentially unreliable labels. Extensive experiments on two benchmark datasets (FewRel and Tacred), contaminated with realistic noise patterns, demonstrate that our RiCL approach substantially outperforms existing integrations of state-of-the-art online continual learning and noisy-label learning methods.