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🚀 New Paper: Pixel Reasoner 🧠🖼️ How can Vision-Language Models (VLMs) perform chain-of-thought reasoning within the image itself? We introduce Pixel Reasoner, the first open-source framework that enables VLMs to “think in pixel space” through curiosity-driven reinforcement learning. Current VLMs reason only in text — even when grounded in...

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Wenhu Chen

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82,829 views • 1 year ago •via X (Twitter)

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Wenhu Chen1 year ago

We found that using standard GRPO does not work well because VLMs tend to ignore these visual operations. Therefore, we propose the curiosity-driven reward to incentivize the model to use visual operations properly but not over-use it. RaPR is the ratio of rollouts in one group that use visual operations. 1_{PR} means whether a specific rollout uses visual operations. H is a threashhold. So the r_curiosity will reward the individual rollout in the groups which have low visual operation rate. r_penalty will penalize the over-use of the visual operations to prevent reward hacking. This reward design is the key to build Pixel Reasoner.

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Wenhu Chen1 year ago

Great work led by Alex Su and Haozhe Wang, in collaboration with HKUST and USTC.

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Rainmaker2 years ago

Can Machine Learning beat the market? Check out this post on my free Substack where I share code and commentary for an XGBoost model and a Random Forest model that both deliver powerful performances.

Chengzu Li's profile picture
Chengzu Li1 year ago

Very cool work! We are also exploring reasoning with image, but through image generation as imagination. If you are interested, feel free to take a look!

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Quizmaster China1 year ago

Congratulations! 加油!

Awsaf's profile picture
Awsaf1 year ago

Wow. Here's another o3 inspired work:

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🙉🙉1 year ago

Very cool!

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