MOSS-VL

MOSS-VL is an open vision-language model family for image and video understanding. Its offline model, MOSS-VL-Instruct, is competitive at comparable scale and leads temporal-reasoning video benchmarks.

As a core contributor, my work on this release covered the training-data stack — infrastructure, assembly and synthesis:

• Built the team’s training-data infrastructure: video/image/text preprocessing and deduplication pipelines, MongoDB schema iteration and data import/export flows
• Owned training-data assembly and versioned delivery for pre-training stages 1–4 and SFT: dataset curation, token statistics, and mixture-ratio sampling
• Designed and implemented image/video caption augmentation schemes (random perturbation, multi-image sequence expansion, multi-image collage, multi-video interleaving, etc.); restructured image-grounding data into multi-turn, multi-image form
• Ran the model identity data synthesis pipeline end to end, producing 200K samples for pre-training stage 4 (long-context and annealing) and 50K for SFT