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![AINativeF Avatar](https://lunarcrush.com/gi/w:24/cr:twitter::1795402815298486272.png) AI Native Foundation [@AINativeF](/creator/twitter/AINativeF) on x 2018 followers
Created: 2025-07-24 00:51:25 UTC

X. ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent Planning

🔑 Keywords: Vision-language-action, Reinforced visual latent planning, Few-shot adaptation, Long-horizon planning, AI Native

💡 Category: Multi-Modal Learning

🌟 Research Objective:
   - The study aims to enhance vision-language-action tasks by developing ThinkAct, a dual-system framework that integrates high-level reasoning with robust action execution.

🛠️ Research Methods:
   - ThinkAct employs reinforced visual latent planning that trains a multimodal large language model to generate embodied reasoning plans, guided by action-aligned visual rewards for improved planning and adaptation.

💬 Research Conclusions:
   - Experiments demonstrate that ThinkAct supports few-shot adaptation, enables long-horizon planning, and promotes self-correction behaviors in complex embodied AI tasks.

👉 Paper link:

![](https://pbs.twimg.com/media/GwlXNxWawAA43Se.jpg)

XX engagements

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[Post Link](https://x.com/AINativeF/status/1948184210088604132)