DataComp-VLM benchmark shows instruction-heavy data mixing outperforms filtering for VLM training, with DCVLM-Baseline achieving 63.6% on 33 tasks for 8B models (+5.4pp over FineVision).
IFBench: Granular Instruction-Following Evaluation,
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Brick routes queries to LLMs using capability scores and difficulty estimates, reaching 76.98% accuracy at max-quality and 4.71x lower cost at neutral profile on 5,504 queries versus always using the strongest model.
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DataComp-VLM: Improved Open Datasets for Vision-Language Models
DataComp-VLM benchmark shows instruction-heavy data mixing outperforms filtering for VLM training, with DCVLM-Baseline achieving 63.6% on 33 tasks for 8B models (+5.4pp over FineVision).
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Brick: Spatial Capability Routing for the Mixture-of-Models (MoM) Paradigm
Brick routes queries to LLMs using capability scores and difficulty estimates, reaching 76.98% accuracy at max-quality and 4.71x lower cost at neutral profile on 5,504 queries versus always using the strongest model.