Preference Optimization for Vision Language Models with TRL
Training models to understand and predict human preferences can be incredibly complex. Traditional methods, like supervised fine-tuning, often require assigning specific labels to data, which is not cost-efficient, especially for nuanced tasks. Preference optimization is an alternative approach that can simplify this process and yield more accurate results. By focusing on comparing and ranking candidate answers rather than assigning fixed labels, preference optimization allows models to capture the subtleties of human judgment more effectively.
Preference optimization is widely used for fine-tuning language models, but it can also be applied to vision language models (VLM).
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