LifeAlign: Lifelong Alignment for Large Language Models with Memory-Augmented Focalized Preference Optimization
This addresses the problem of knowledge loss in LLMs for AI developers and researchers, representing an incremental improvement in lifelong learning techniques.
The paper tackles catastrophic forgetting in Large Language Models during sequential alignment tasks by introducing LifeAlign, a lifelong alignment framework that maintains consistent human preference alignment across domains, achieving superior performance in both alignment quality and knowledge retention compared to existing methods.
Alignment plays a crucial role in Large Language Models (LLMs) in aligning with human preferences on a specific task/domain. Traditional alignment methods suffer from catastrophic forgetting, where models lose previously acquired knowledge when adapting to new preferences or domains. We introduce LifeAlign, a novel framework for lifelong alignment that enables LLMs to maintain consistent human preference alignment across sequential learning tasks without forgetting previously learned knowledge. Our approach consists of two key innovations. First, we propose a focalized preference optimization strategy that aligns LLMs with new preferences while preventing the erosion of knowledge acquired from previous tasks. Second, we develop a short-to-long memory consolidation mechanism that merges denoised short-term preference representations into stable long-term memory using intrinsic dimensionality reduction, enabling efficient storage and retrieval of alignment patterns across diverse domains. We evaluate LifeAlign across multiple sequential alignment tasks spanning different domains and preference types. Experimental results demonstrate that our method achieves superior performance in maintaining both preference alignment quality and knowledge retention compared to existing lifelong learning approaches. The codes and datasets will be released on GitHub.