Prompt-Sharing Framework Proposed to Enhance Continual Learning Efficiency

Global: Prompt-Sharing Framework Proposed to Enhance Continual Learning Efficiency

A team of machine‑learning researchers published a new study on January 28, 2026, introducing a prompt‑sharing framework designed to improve the efficiency of continual learning systems. The paper, authored by Jiangyang Li, Chenhao Ding, Songlin Dong, Qiang Wang, Jianchao Zhao, Yuhang He, and Yihong Gong, addresses limitations in existing prompt‑based methods that allocate a fixed set of prompts to each task.

Background on Prompt‑Based Continual Learning

Prompt‑based continual learning aims to mitigate catastrophic forgetting by using task‑specific prompts to guide model updates. While effective, many approaches isolate prompts per task, which can lead to underutilized parameters and reduced scalability as the number of tasks grows.

Introducing a Prompt‑Sharing Framework

The authors propose a global prompt pool that enables multiple tasks to draw from a shared repository of prompts. This design encourages collaborative optimization of task‑specific feature representations while maintaining the flexibility to adapt to new tasks.

Task‑Aware Gated Routing Mechanism

Central to the framework is a task‑aware gated routing mechanism that sparsely activates a subset of prompts for each incoming task. By dynamically decoupling prompt selection, the system balances isolation and sharing, allowing tasks to benefit from shared knowledge without excessive interference.

History‑Aware Modulator for Stable Updates

To protect frequently used prompts from over‑adjustment, the study introduces a history‑aware modulator that leverages cumulative activation statistics. This component reduces the risk of inefficient parameter updates and helps preserve learned knowledge across the task sequence.

Empirical Evaluation

Extensive experiments reported in the paper demonstrate that the prompt‑sharing approach consistently outperforms static allocation strategies in both effectiveness and computational efficiency. The authors cite improvements in accuracy and reduced training time across several benchmark continual‑learning datasets.

Implications and Future Work

The findings suggest that dynamic prompt allocation can address long‑standing challenges in continual learning, particularly regarding parameter utilization and knowledge retention. The researchers indicate plans to explore larger-scale deployments and to integrate the framework with other adaptive learning paradigms.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.