******OptiMer Continual Pre-Training******
Key Questions
What is OptiMer in continual pre-training?
OptiMer uses vector merging over data mixing for continual pre-training, avoiding data-related issues. It targets drift and offline pains in model training.
Why prefer vector merging in continual pre-training?
Vector merging outperforms data mixing by preventing data contamination and other issues in extending pre-training. This method maintains model performance without rigid stage separations.
What does Ari Morcos advocate for model training?
Ari Morcos pushes unified training over distinct, independent stages, as the joint is critical. This addresses gaps between pretraining and other phases to reduce drift.
What challenges does continual pre-training solve?
Continual pre-training tackles data issues, drift, and offline problems via techniques like vector merging. It enables seamless extension of model capabilities.
How does mid-training RL fit into training advances?
Thinking Mid-training uses RL for interleaved reasoning, bridging pretraining gaps without explicit supervision. This supports more fluid training paradigms beyond rigid stages.
Vector merging > data mixing for continual pre-training sans data issues; Ari Morcos unified training over rigid stages targets drift/offline pains.