Turning Back Without Forgetting: Selective Backward Refinement for Parameter-Efficient Continual Learning
arXiv:2606.01379v1 Announce Type: new Abstract: While prompt-based parameter-efficient continual learning mitigates catastrophic forgetting by isolating task-specific prompts, this isolation also limits later tasks from improving earlier ones, leaving backward knowledge transfer underexplored. We address this limitation by proposing Selective bAckward refinement for positive Backward knowledge transfER (SABER), a replay-free framework that enables controlled backward transfer in prompt-based continual learning. SABER determines when backward refinement is beneficial using complementary task-co
The increasing complexity and scale of AI models necessitate more efficient and adaptable learning paradigms to overcome limitations like catastrophic forgetting in continual learning.
Improving continual learning efficiency and knowledge transfer could accelerate AI development, making models more robust and adaptable over time without constant retraining from scratch.
This research suggests a shift towards AI systems that can learn new tasks while selectively refining past knowledge, potentially leading to more flexible and knowledge-retentive AI agents.
- · AI development platforms
- · Continual learning researchers
- · Companies deploying dynamic AI systems
- · AI models requiring frequent retraining
- · Resource-intensive continual learning methods
More efficient and adaptable AI models become feasible, especially for long-lived systems.
Reduced computational costs and environmental impact associated with AI model updates and maintenance could be realized.
The development of truly 'ever-learning' AI agents capable of sustained, high-performance operation in dynamic environments might be accelerated.
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Read at arXiv cs.LG