Continual Learning for LLMs: Merge Before Forgetting with LoRA
Research Paper#Continual Learning, LLMs, LoRA🔬 Research|Analyzed: Jan 3, 2026 19:20•
Published: Dec 28, 2025 17:37
•1 min read
•ArXivAnalysis
This paper addresses the challenge of catastrophic forgetting in large language models (LLMs) within a continual learning setting. It proposes a novel method that merges Low-Rank Adaptation (LoRA) modules sequentially into a single unified LoRA, aiming to improve memory efficiency and reduce task interference. The core innovation lies in orthogonal initialization and a time-aware scaling mechanism for merging LoRAs. This approach is particularly relevant because it tackles the growing computational and memory demands of existing LoRA-based continual learning methods.
Key Takeaways
- •Proposes a novel continual learning method for LLMs using LoRA.
- •Employs orthogonal initialization and time-aware scaling for merging LoRAs.
- •Aims to improve memory efficiency and reduce task interference.
- •Maintains constant memory complexity with respect to the number of tasks.
Reference / Citation
View Original"The method leverages orthogonal basis extraction from previously learned LoRA to initialize the learning of new tasks, further exploits the intrinsic asymmetry property of LoRA components by using a time-aware scaling mechanism to balance new and old knowledge during continual merging."