Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning
arXiv:2603.12478v2 Announce Type: replace-cross Abstract: Multimodal instruction tuning is often compute-inefficient because training budgets are spread across large mixed image-video pools whose utility is highly uneven. We present Goal-Driven Data Optimization (GDO), a framework that computes six sample descriptors for each candidate and constructs optimized 1$\times$ training subsets for different goals. Under a fixed one-epoch Qwen3-VL-8B-Instruct training and evaluation recipe on 8 H20 GPUs, GDO uses far fewer training samples than the Uni-10x baseline while converging faster and achievin
The increasing computational demands of multimodal AI models necessitate more efficient data handling to continue progress without prohibitive costs.
This development allows for significantly faster and more cost-effective training of large multimodal models, impacting the pace and accessibility of advanced AI development.
AI model training can now achieve high performance with substantially less data and computation, accelerating iteration cycles and reducing infrastructure requirements.
- · AI developers
- · Cloud providers
- · AI startups
- · Hardware manufacturers (indirectly via increased efficiency)
- · Companies with less efficient data optimization strategies
- · Those reliant on brute-force, data-intensive training methods
Faster and cheaper development of multimodal AI applications.
Increased competition and innovation in AI as barriers to entry are lowered.
Potential for more diverse and specialized AI models tailored to specific tasks, requiring less monolithic infrastructure.
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Read at arXiv cs.LG