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GAP-Prompt Boosts Continual Learning, Reduces Forgetting

Trung-Anh Dang, Duy-Cuong Bui, Ngoc-Son Vu, Christel Vrain, Vincent Nguyen· August 26, 2026 View original

Key takeaways

  • GAP-Prompt effectively mitigates catastrophic forgetting in continual learning.
  • It introduces instance-level adaptive prompting for fine-grained knowledge retention.
  • Dynamic knowledge fusion and shared prompt distillation are key components for performance.
  • The method achieves state-of-the-art results on various benchmarks, approaching joint training performance.

Who benefits

AI/ML DevelopmentRoboticsAutonomous SystemsHealthcareEdTech

Summary

This paper proposes Gated Adaptive Prompting (GAP-Prompt), a novel method for continual learning that mitigates catastrophic forgetting. It achieves this through instance-level adaptive prompting, dynamic knowledge fusion, and shared prompt distillation, significantly outperforming existing methods.

Continual learning, where models learn new tasks sequentially without forgetting old ones, faces the significant challenge of catastrophic forgetting. While prompt-based methods with frozen backbones offer a solution, they often use static, task-level prompting, overlooking the diversity within tasks. This research introduces Gated Adaptive Prompting (GAP-Prompt) to address this limitation. GAP-Prompt incorporates instance-level adaptability into the prompting process through three synergistic modules. First, instance-conditioned gating dynamically selects optimal prompt injection layers for each individual input. Second, dynamic knowledge fusion aggregates current and historical prompts in an instance-aware manner, facilitating knowledge integration across tasks. Third, shared prompt distillation anchors foundational knowledge in early shared layers to further prevent forgetting. Extensive evaluations on CIFAR-100, ImageNet-R, and CUB-200 benchmarks demonstrate that GAP-Prompt consistently achieves state-of-the-art performance. Notably, on the fine-grained CUB-200 dataset, it approaches the joint training upper bound, significantly outperforming existing continual learning methods.

Why it matters

Professionals building AI systems that need to continuously learn and adapt to new data or tasks without retraining from scratch can leverage GAP-Prompt to overcome catastrophic forgetting, leading to more robust and efficient models.

How to implement this in your domain

  1. 1Evaluate GAP-Prompt for AI models requiring continuous updates and learning in dynamic environments.
  2. 2Integrate instance-level adaptive prompting into existing continual learning pipelines to improve performance.
  3. 3Experiment with dynamic knowledge fusion and shared prompt distillation in models facing catastrophic forgetting.
  4. 4Apply GAP-Prompt in applications where models must adapt to evolving data streams or new task requirements.

Original post by Trung-Anh Dang, Duy-Cuong Bui, Ngoc-Son Vu, Christel Vrain, Vincent Nguyen

"arXiv:2608.23782v1 Announce Type: new Abstract: Continual learning faces the persistent challenge of catastrophic forgetting, where sequential task updates degrade previously acquired knowledge. While prompt-based methods integrated with pre-trained models offer a compelling solu…"

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Originally posted by Trung-Anh Dang, Duy-Cuong Bui, Ngoc-Son Vu, Christel Vrain, Vincent Nguyen on X · view source

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