RELEASE · MODELS · #2532
Nvidia open-sources Flora, a Qwen3.5-based robot video labeling engine
Nvidia's GEAR lab open-sourced Flora, a data-engine composed of three fine-tuned Qwen3.5 models (9B for segmentation, 27B for descriptions, and a 27B 'Flora-Rewarder' for scoring) that labels robot videos for action boundaries, step-level rewards, and task success. Nvidia says Flora is already used in training its GR00T models at scale on GB300 GPUs and estimates labeling costs of about $0.72/hour versus ~$88/hour with GPT-6 Astra, though the cost figures are projections and benchmark comparisons include methodological caveats.
KEY POINTS
- Nvidia's GEAR lab open-sourced Flora, a data-engine composed of three fine-tuned Qwen3.5 models (9B for segmentation, 27B for descriptions, and a 27B 'Flora-Rewarder' for scoring) that labels robot videos for action boundaries, step-level rewards, and task success.
- Nvidia says Flora is already used in training its GR00T models at scale on GB300 GPUs and estimates labeling costs of about $0.72/hour versus ~$88/hour with GPT-6 Astra, though the cost figures are projections and benchmark comparisons include methodological caveats.
- An open, scalable video-labeling stack tailored to robotics could cut the cost and increase the scale of training data, helping developers and Nvidia's hardware ecosystem — but the claimed savings and benchmark leads are based on projections and specific tests.
WHY IT MATTERS
An open, scalable video-labeling stack tailored to robotics could cut the cost and increase the scale of training data, helping developers and Nvidia's hardware ecosystem — but the claimed savings and benchmark leads are based on projections and specific tests.