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Training Vision Language Models

Learn how to train and fine-tune VLAs for robotics tasks.

  • CLIP: Vision-language understanding
  • BLIP-2: Efficient vision-language alignment
  • GPT-4V: Multimodal reasoning
  • RT-2: Robotics Transformer 2

Training Pipeline​

  1. Data collection (images + captions/actions)
  2. Preprocessing and augmentation
  3. Model selection and configuration
  4. Training loop with loss functions
  5. Evaluation and validation
  6. Fine-tuning for robotics tasks

Best Practices​

  • Use diverse, representative datasets
  • Implement robust data augmentation
  • Monitor loss curves and gradients
  • Validate on held-out test sets