mSFT: Addressing Dataset Mixtures Overfitting Heterogeneously in Multi-task SFT
📰 ArXiv cs.AI
mSFT addresses overfitting in multi-task supervised fine-tuning by iteratively adjusting dataset mixtures
Action Steps
- Identify heterogeneous learning dynamics in multi-task models
- Apply mSFT to iteratively adjust dataset mixtures and prevent overfitting
- Monitor and evaluate model performance on each task
- Adjust compute budget allocation based on task-specific learning speeds
Who Needs to Know This
Machine learning researchers and engineers on a team can benefit from mSFT to improve the performance of their multi-task models, and product managers can utilize this technique to optimize model training for various tasks
Key Insight
💡 Heterogeneous learning dynamics can lead to overfitting and underfitting in multi-task models, which can be addressed by adjusting dataset mixtures
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🚀 mSFT: combating overfitting in multi-task SFT with iterative dataset mixture adjustments
Key Takeaways
mSFT addresses overfitting in multi-task supervised fine-tuning by iteratively adjusting dataset mixtures
Full Article
Title: mSFT: Addressing Dataset Mixtures Overfitting Heterogeneously in Multi-task SFT
Abstract:
arXiv:2603.21606v2 Announce Type: replace-cross Abstract: Current language model training commonly applies multi-task Supervised Fine-Tuning (SFT) using a homogeneous compute budget across all sub-datasets. This approach is fundamentally sub-optimal: heterogeneous learning dynamics cause faster-learning tasks to overfit early while slower ones remain under-fitted. To address this, we introduce mSFT, an iterative, overfitting-aware search algorithm for multi-task data mixtures. mSFT trains the mo
Abstract:
arXiv:2603.21606v2 Announce Type: replace-cross Abstract: Current language model training commonly applies multi-task Supervised Fine-Tuning (SFT) using a homogeneous compute budget across all sub-datasets. This approach is fundamentally sub-optimal: heterogeneous learning dynamics cause faster-learning tasks to overfit early while slower ones remain under-fitted. To address this, we introduce mSFT, an iterative, overfitting-aware search algorithm for multi-task data mixtures. mSFT trains the mo
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