OASIS: Online Sample Selection for Continual Visual Instruction Tuning

• URL: http://arxiv.org/abs/2506.02011v1
• Date: Tue, 27 May 2025 20:32:43 GMT
• Title: OASIS: Online Sample Selection for Continual Visual Instruction Tuning
• Authors: Minjae Lee, Minhyuk Seo, Tingyu Qu, Tinne Tuytelaars, Jonghyun Choi,
• Abstract summary: We propose an adaptive online sample selection approach for visual instruction tuning.<n>OASIS dynamically adjusts selected samples per batch based on relative inter-batch informativeness.<n>It achieves comparable performance to full-data training using only 25% of the data and outperforms the state-of-the-art.
• Score: 42.60893812402742
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In continual visual instruction tuning (CVIT) scenarios, where multi-modal data continuously arrive in an online streaming manner, training delays from large-scale data significantly hinder real-time adaptation. While existing data selection strategies reduce training overheads, they rely on pre-trained reference models, which are impractical in CVIT setups due to unknown future data. Recent reference model-free online sample selection methods address this issue but typically select a fixed number of samples per batch (e.g., top-k), causing them to suffer from distribution shifts where informativeness varies across batches. To address these limitations, we propose OASIS, an adaptive online sample selection approach for CVIT that: (1) dynamically adjusts selected samples per batch based on relative inter-batch informativeness, and (2) minimizes redundancy of selected samples through iterative selection score updates. Empirical results across various MLLMs, such as LLaVA-1.5 and Qwen-VL-2.5, show that OASIS achieves comparable performance to full-data training using only 25% of the data and outperforms the state-of-the-art.

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