Is Parameter Isolation Better for Prompt-Based Continual Learning?
This addresses the practical need for better parameter efficiency in continual learning systems, though it appears incremental as it builds on existing prompt-based methods.
The paper tackles the problem of suboptimal parameter utilization in prompt-based continual learning methods by proposing a prompt-sharing framework with task-aware gated routing and a history-aware modulator, achieving consistent improvements over static allocation strategies in effectiveness and efficiency.
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.