Two-Stage Optimizer-Aware Online Data Selection for Large Language Models
📰 ArXiv cs.AI
Two-stage optimizer-aware online data selection for large language models improves fine-tuning efficiency
Action Steps
- Identify the limitations of existing gradient-based data selection methods in offline settings
- Develop a two-stage optimizer-aware framework for online data selection
- Implement the framework to adapt to sequential data arrival and step-dependent sample utility
- Evaluate the effectiveness of the framework in improving fine-tuning efficiency
Who Needs to Know This
ML researchers and engineers working on large language models can benefit from this framework to optimize data selection and improve model performance
Key Insight
💡 Optimizer-aware online data selection can significantly improve the efficiency of large language model fine-tuning
Share This
🚀 Improve LLM fine-tuning with two-stage optimizer-aware online data selection!
Key Takeaways
Two-stage optimizer-aware online data selection for large language models improves fine-tuning efficiency
Full Article
Title: Two-Stage Optimizer-Aware Online Data Selection for Large Language Models
Abstract:
arXiv:2604.00001v1 Announce Type: cross Abstract: Gradient-based data selection offers a principled framework for estimating sample utility in large language model (LLM) fine-tuning, but existing methods are mostly designed for offline settings. They are therefore less suited to online fine-tuning, where data arrives sequentially, sample utility is step-dependent, and the effective update geometry is shaped by adaptive optimizers. We propose an optimizer-aware framework for gradient-based online
Abstract:
arXiv:2604.00001v1 Announce Type: cross Abstract: Gradient-based data selection offers a principled framework for estimating sample utility in large language model (LLM) fine-tuning, but existing methods are mostly designed for offline settings. They are therefore less suited to online fine-tuning, where data arrives sequentially, sample utility is step-dependent, and the effective update geometry is shaped by adaptive optimizers. We propose an optimizer-aware framework for gradient-based online
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