Diversity Vs Density: A data strategy comparison for fine-tuning VLMs (huggingface.co)

A recent exploration into fine-tuning Visual Language Models (VLMs) reveals a critical comparison between two data strategies: diversity and density. The study aims to optimize model training in contexts where image datasets are limited, particularly in sectors like robotics and banking. The diversity approach emphasizes presenting a wide range of images and related questions, while the density strategy focuses on generating multiple questions about fewer images. The findings indicate that the diversity strategy delivers superior performance, outperforming density models by at least 3.2%. However, when scaled correctly, the density approach can yield competitive results, offering a promising solution for domains with restricted data access. Notably, the research underscores the importance of how language models interpret visual information rather than just relying on image variety. Even though the density strategy appears efficient, especially for non-reasoning models, reasoning capabilities could suffer from overfitting and logical inconsistencies. The study's results suggest that while it’s feasible to train effectively on a smaller image subset, a mix of both strategies may enhance model robustness by preventing overfitting to specific visual styles. As the research progresses, exploring larger datasets and nuanced density scales may reveal optimal strategies for future VLM training.