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About this paper
Jinghan He, Junfeng Fang, Feng Xiong, Zijun Yao, Fei Shen, Haiyun Guo, Jinqiao Wang, Tat-Seng Chua
Active-Zero introduces a framework enabling Vision-Language Models (VLMs) to autonomously improve their reasoning by actively exploring and curating visual data from open-ended environments. This approach allows VLMs to dynamically construct a learning curriculum tailored to their evolving capabilities, achieving superior performance on reasoning and general visual understanding tasks.
Key insights
- Empowering Vision-Language Models (VLMs) to actively explore and curate visual data from open environments significantly overcomes the limitations of static datasets.
- The co-evolutionary training of specialized agents (Searcher, Questioner, Solver) enables the creation of a self-scaffolding curriculum that continuously adapts to the VLM's evolving capabilities.
- Optimizing data acquisition for a balance of challenge (based on Solver uncertainty) and diversity (through repetition penalties) is crucial for generating an effective and comprehensive learning trajectory.
Summary by alphaXiv.
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