Boosting Large Language Models with Mask Fine-Tuning

Name: Boosting Large Language Models with Mask Fine-Tuning
Uploaded: 2026-10-08T03:19:21.340753+00:00
Duration: 765 s
Description: Mask Fine-Tuning (MFT) is a method that improves large language models by learning a binary mask to remove specific weights from an already well-trained model. By using the standard fine-tuning objective, MFT identifies and removes parameters, leading to performance gains across various tasks without needing new data or significant computational overhead.

Mask Fine-Tuning (MFT) is a method that improves large language models by learning a binary mask to remove specific weights from an already well-trained model. By using the standard fine-tuning objective, MFT identifies and removes parameters, leading to performance gains across various tasks without needing new data or significant computational overhead.

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