Can vision language models learn intuitive physics from interaction?
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Recent research indicates that pre-trained vision-language models struggle with physical world intuitions. Although supervised fine-tuning enhances performance on simple tasks, it doesn't yield robust, generalizable physical rules. Experiments using reinforcement learning for interaction-based training improved task-specific performance but failed to ensure generalization across related tasks, even when visual and physical similarities existed.
Vision Language Models Struggle with Intuitive Physics, Research Reveals
Recent research indicates that pre-trained vision language models lack a fundamental understanding of physical dynamics, despite efforts to enhance their capabilities through supervised fine-tuning. These models show improved performance on basic physical tasks, but the enhancements do not extend to robust generalizations across varied contexts.
Key Findings on Model Performance
One significant outcome is that models trained on specific tasks fail to transfer their learning effectively to related tasks, even when those tasks share similar visual statistics and underlying physical principles. This gap underscores the limitations of current training methodologies that rely on interaction without fostering broader understanding.
While reinforcement learning can enhance immediate task performance, it does not equip models with the tools to apply learned concepts in diverse scenarios. This raises questions about the efficacy of existing training frameworks for developing intuitive physics in AI systems.
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📰 Original Source: https://arxiv.org/abs/2602.06033v1
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