MIDUS: Memory-Infused Depth Up-Scaling
📰 ArXiv cs.AI
Learn how to implement Memory-Infused Depth Up-Scaling (MIDUS) to increase pre-trained language model capacity without training from scratch
Action Steps
- Implement Depth Up-Scaling (DUS) by duplicating Transformer blocks in a pre-trained model
- Insert duplicated blocks into the model backbone using a block-level dense residual branch
- Apply memory infusion to reduce parameter and compute cost while maintaining added capacity
- Test the MIDUS model on a benchmark dataset to evaluate performance improvements
- Compare the results with the original pre-trained model and other up-scaling methods
Who Needs to Know This
NLP engineers and researchers can benefit from this technique to improve model performance without extensive retraining
Key Insight
💡 MIDUS combines the benefits of Depth Up-Scaling with memory infusion to efficiently increase model capacity
Share This
🚀 Boost pre-trained language model capacity with Memory-Infused Depth Up-Scaling (MIDUS) 🚀
Key Takeaways
Learn how to implement Memory-Infused Depth Up-Scaling (MIDUS) to increase pre-trained language model capacity without training from scratch
Full Article
Title: MIDUS: Memory-Infused Depth Up-Scaling
Abstract:
arXiv:2512.13751v2 Announce Type: replace-cross Abstract: Expanding pre-trained language models offers a practical way to increase capacity without training larger models from scratch. Depth Up-Scaling (DUS) does so by duplicating Transformer blocks and inserting them into a pre-trained backbone. This process also duplicates FFN-heavy blocks, increasing parameter and compute cost while adding capacity through a block-level dense residual branch. Yet prior work suggests that added capacity need n
Abstract:
arXiv:2512.13751v2 Announce Type: replace-cross Abstract: Expanding pre-trained language models offers a practical way to increase capacity without training larger models from scratch. Depth Up-Scaling (DUS) does so by duplicating Transformer blocks and inserting them into a pre-trained backbone. This process also duplicates FFN-heavy blocks, increasing parameter and compute cost while adding capacity through a block-level dense residual branch. Yet prior work suggests that added capacity need n
DeepCamp AI