Is Parameter Isolation Better for Prompt-Based Continual Learning?

TL;DR

This paper proposes a prompt-sharing framework with dynamic prompt routing and a history-aware modulator to improve parameter utilization and reduce forgetting in prompt-based continual learning.

Contribution

It introduces a novel prompt-sharing approach with task-aware gating and history-aware modulation, enhancing efficiency and effectiveness over static prompt allocation methods.

Findings

Outperforms existing static prompt allocation strategies

Improves parameter efficiency and task performance

Reduces catastrophic forgetting in continual learning

Abstract

Prompt-based continual learning methods effectively mitigate catastrophic forgetting. However, most existing methods assign a fixed set of prompts to each task, completely isolating knowledge across tasks and resulting in suboptimal parameter utilization. To address this, we consider the practical needs of continual learning and propose a prompt-sharing framework. This framework constructs a global prompt pool and introduces a task-aware gated routing mechanism that sparsely activates a subset of prompts to achieve dynamic decoupling and collaborative optimization of task-specific feature representations. Furthermore, we introduce a history-aware modulator that leverages cumulative prompt activation statistics to protect frequently used prompts from excessive updates, thereby mitigating inefficient parameter usage and knowledge forgetting. Extensive analysis and empirical results demonstrate that our approach consistently outperforms existing static allocation strategies in effectiveness and efficiency.