Mixture-of-Skills: Learning to Optimize Data Usage for Fine-Tuning Large Language Models (2024.emnlp-main)
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| Challenge: | Large language models are fine-tuned on diverse datasets to develop a range of skills . each skill has unique characteristics, and datasets are heterogeneous and imbalanced . a general, model-agnostic, reinforcement learning framework is proposed to optimize data usage . |
| Approach: | They propose a general, model-agnostic, reinforcement learning framework that optimizes data usage automatically during the fine-tuning process. |
| Outcome: | The proposed framework optimizes data usage automatically during the fine-tuning process. |
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