Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization
Paper
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2411.10442
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Published
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60
Symbol-LLM
Symbol-LLM
AI & ML interests
Natural Language Processing, Large Language Models, Neuro-Symbolic
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🥳 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 !
Reacted to
their
post
with 🔥
7 days ago
🥳 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 !
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🥳 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: Vision-Language Models Can Self-Improve Reasoning via Reflection (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 !
📕 Title: Vision-Language Models Can Self-Improve Reasoning via Reflection
🔗 Link: Vision-Language Models Can Self-Improve Reasoning via Reflection (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 !
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7 days ago
Post
891
🥳 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: Vision-Language Models Can Self-Improve Reasoning via Reflection (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 !
📕 Title: Vision-Language Models Can Self-Improve Reasoning via Reflection
🔗 Link: Vision-Language Models Can Self-Improve Reasoning via Reflection (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 !
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Exciting to see open-source models thriving in the computer agent space! 🔥
I just built a demo for OS-ATLAS: A Foundation Action Model For Generalist GUI Agents — check it out here: maxiw/OS-ATLAS
This demo predicts bounding boxes based on screenshot + instructions as input.
I just built a demo for OS-ATLAS: A Foundation Action Model For Generalist GUI Agents — check it out here: maxiw/OS-ATLAS
This demo predicts bounding boxes based on screenshot + instructions as input.
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