Continual Learning in Large Language Models: A Comprehensive Survey

This research survey explores continual learning (CL) as a vital strategy for allowing large language models to acquire new knowledge without suffering from catastrophic forgetting. The authors organize the field into three primary stages: continual pre-training, continual fine-tuning, and continual alignment, detailing how models adapt to new data while maintaining prior expertise. By examining rehearsal-based, regularization-based, and architecture-based methodologies, the text illustrates how traditional machine learning techniques are evolved to meet the massive scale of modern AI. The paper also provides a rigorous overview of evaluation metrics like forgetting rates and introduces essential benchmarks for measuring long-term performance. Ultimately, the sources identify persistent challenges and future opportunities in achieving lifelong learning for more autonomous and dynamic language systems.linktree: https://linktr.ee/learnbydoingwithstevenarvix: https://arxiv.org/pdf/2603.12658