Data-Juicer Sandbox: A Comprehensive Suite for Multimodal Data-Model
  Co-development
The emergence of large-scale multi-modal generative models has drastically
advanced artificial intelligence, introducing unprecedented levels of
performance and functionality. However, optimizing these models remains
challenging due to historically isolated paths of model-centric and
data-centric developments, leading to suboptimal outcomes and inefficient
resource utilization. In response, we present a novel sandbox suite tailored
for integrated data-model co-development. This sandbox provides a comprehensive
experimental platform, enabling rapid iteration and insight-driven refinement
of both data and models. Our proposed "Probe-Analyze-Refine" workflow,
validated through applications on state-of-the-art LLaVA-like and DiT based
models, yields significant performance boosts, such as topping the VBench
leaderboard. We also uncover fruitful insights gleaned from exhaustive
benchmarks, shedding light on the critical interplay between data quality,
diversity, and model behavior. With the hope of fostering deeper understanding
and future progress in multi-modal data and generative modeling, our codes,
datasets, and models are maintained and accessible at
https://github.com/modelscope/data-juicer/blob/main/docs/Sandbox.md.
669739baa7fd582ae7420a37
Daoyuan Chen
669739baa7fd582ae7420a38
Haibin Wang
669739baa7fd582ae7420a39
Yilun Huang
claimed_verified
2025-07-07T11:27:38.721Z
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/avatars/46a34fe25b139acf59f8e5a665999ba1.svg
Dylan Huang
user
hylcool
669739baa7fd582ae7420a3a
Ce Ge
669739baa7fd582ae7420a3b
Yaliang Li
669739baa7fd582ae7420a3c
Bolin Ding
669739baa7fd582ae7420a3d
Jingren Zhou
Data-Juicer Sandbox: A Comprehensive Suite for Multimodal Data-Model
  Co-development
The emergence of large-scale multi-modal generative models has drastically
advanced artificial intelligence, introducing unprecedented levels of
performance and functionality. However, optimizing these models remains
challenging due to historically isolated paths of model-centric and
data-centric developments, leading to suboptimal outcomes and inefficient
resource utilization. In response, we present a novel sandbox suite tailored
for integrated data-model co-development. This sandbox provides a comprehensive
experimental platform, enabling rapid iteration and insight-driven refinement
of both data and models. Our proposed "Probe-Analyze-Refine" workflow,
validated through applications on state-of-the-art LLaVA-like and DiT based
models, yields significant performance boosts, such as topping the VBench
leaderboard. We also uncover fruitful insights gleaned from exhaustive
benchmarks, shedding light on the critical interplay between data quality,
diversity, and model behavior. With the hope of fostering deeper understanding
and future progress in multi-modal data and generative modeling, our codes,
datasets, and models are maintained and accessible at
https://github.com/modelscope/data-juicer/blob/main/docs/Sandbox.md.
A novel sandbox suite for integrated data-model co-development enhances performance in multi-modal generative models, improving data quality and model behavior through a "Probe-Analyze-Refine" workflow.
multi-modal generative models sandbox suite data-model co-development Probe-Analyze-Refine LLaVA DiT VBench leaderboard