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arXiv · 2508.10339

Concepts or Skills? Rethinking Instruction Selection for Multi-modal Models

Andrew Bai·Justin Cui·Ruochen Wang·Cho-Jui Hsieh
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Abstract

Vision-language instruction tuning achieves two main purposes: learning visual concepts and learning visual skills. In this paper, we found that vision-language benchmarks fall into the dichotomy of mainly benefiting from training on instructions with similar skills or visual concepts. Inspired by the discovery, we designed a simple targeted training data selection method to optimize the performance of a given benchmark. We first extract the concepts/skills from the benchmark, determine whether the benchmark predominantly benefits from similar concepts or skills, and finally select instructions with the most matching concepts/skills. Experiments on 10+ benchmarks validate the effectiveness of our targeted data selection method, showing +0.9\% over the best existing baseline averaged over all benchmarks and +1.5\% on the skill-focused subset. Our findings underscore the importance of recognizing the inherent trade-off within instruction selection, which requires balancing the acquisition of conceptual knowledge against visual skill.

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Andrew Bai, Justin Cui, Ruochen Wang, Cho-Jui Hsieh. 2025-08-14. Concepts or Skills? Rethinking Instruction Selection for Multi-modal Models. https://arxiv.org/abs/2508.10339

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