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tianchez 
posted an update 12 days ago
Post
4008
Introducing VLM-R1!

GRPO has helped DeepSeek R1 to learn reasoning. Can it also help VLMs perform stronger for general computer vision tasks?

The answer is YES and it generalizes better than SFT. We trained Qwen 2.5 VL 3B on RefCOCO (a visual grounding task) and eval on RefCOCO Val and RefGTA (an OOD task).

https://github.com/om-ai-lab/VLM-R1

Great job guys, reasoning bringing so many potential!

we also have similiar idea! but only applied for maze

https://huggingface.co/homebrewltd/AlphaMaze-v0.2-1.5B

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looks very cool!

Hi thanks a lot sharing, I tried a similar approach for making the vlm point to objects in the image, in x y co ordinates using the pixmo points dataset. But inspite of training on around 20k subset of the dataset, the model just produces random x y values and is not improving the reward at all beyond a certain point. I am using a format reward similar to you, and the distance between predicted point and truth as reward I.e. exp(-distance) . It just doesn’t work!! Do you have any insights why it doesn’t work for pointing ? I used qwen2vl 2b.