content.raw: 🥳 Thrilled to introduce our recent efforts on bootstrapping VLMs for multi-modal chain-of-thought reasoning !
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content.value: 🥳 Thrilled to introduce our recent efforts on bootstrapping VLMs for multi-modal chain-of-thought reasoning !
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content.raw: 📕 Title: Vision-Language Models Can Self-Improve Reasoning via Reflection
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content.value: 📕 Title: Vision-Language Models Can Self-Improve Reasoning via Reflection
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content.raw: 🔗 Link: 
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content.value: 🔗 Link: 
content.label: Vision-Language Models Can Self-Improve Reasoning via Reflection (2411.00855)
content.raw: https://huggingface.co/papers/2411.00855
content.id: 2411.00855
content.type: paper
content.type: resource
content.url: https://huggingface.co/papers/2411.00855
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content.raw: 😇Takeaways:
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content.value: 😇Takeaways:
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content.raw: - We found that VLMs can self-improve reasoning performance through a reflection mechanism, and importantly, this approach can scale through test-time computing.
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content.value: - We found that VLMs can self-improve reasoning performance through a reflection mechanism, and importantly, this approach can scale through test-time computing.
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content.raw: - Evaluation on comprehensive and diverse Vision-Language reasoning tasks are included !
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content.value: - Evaluation on comprehensive and diverse Vision-Language reasoning tasks are included !
rawContent: 🥳 Thrilled to introduce our recent efforts on bootstrapping VLMs for multi-modal chain-of-thought reasoning !
  
  📕 Title: Vision-Language Models Can Self-Improve Reasoning via Reflection
  
  🔗 Link: https://huggingface.co/papers/2411.00855
  
  😇Takeaways:
  
  - We found that VLMs can self-improve reasoning performance through a reflection mechanism, and importantly, this approach can scale through test-time computing.
  
  - Evaluation on comprehensive and diverse Vision-Language reasoning tasks are included !
reactions.reaction: 🔥
reactions.users: Symbol-LLM SaylorTwift cckevinn davanstrien ai-everyday
reactions.reaction: 🚀
reactions.users: Symbol-LLM cckevinn John6666