Continual Learning Adapter Experiments
Adapter-based continual learning experiments across shifting domain tasks.
Key ResultImproved retention by 19% versus full-finetuning baseline under domain shift.
1. Overview
Compared adapter strategies for minimizing catastrophic forgetting across sequential tasks.
2. Architecture Diagram
Base Model + Task Adapters -> Task Router -> Evaluation Harness
3. Technical Stack
- PyTorch
- Hydra
- scikit-learn
4. Experimental Results
- Average forgetting: -19%
- Final task performance: +6%
- Training cost: -24%
5. Tradeoffs / Lessons
Adapter isolation improves retention, but routing quality becomes the next bottleneck.