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About this paper
Tuan Van Vo, Tan Quang Nguyen, Khang Minh Nguyen, Duy Ho Minh Nguyen, Minh Nhat Vu
ReFineVLA enhances Vision-Language-Action models by integrating explicit multimodal reasoning through a teacher-guided fine-tuning framework. This approach improves robotic manipulation performance, particularly in complex tasks, and offers increased interpretability by enabling robots to provide structured rationales for their actions.
Key insights
- Injecting explicit, structured multimodal reasoning into pre-trained VLA models using a teacher-guided fine-tuning framework enhances their capabilities.
- Selective parameter fine-tuning, freezing lower layers while training upper ones, effectively preserves pre-trained generalization while developing new reasoning abilities.
- Balancing action prediction and reasoning generation through a multi-objective loss function (optimal reasoning loss weight at 0.3) is crucial for improved performance.
Summary by alphaXiv.
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