GRPO Fine-Tuning for Vision Models

소개

This skill provides a comprehensive implementation framework for GRPO fine-tuning, a reinforcement learning approach that excels when traditional Supervised Fine-Tuning (SFT) underperforms or training data is limited to fewer than 1,000 examples. It enables Claude to guide developers through creating custom reward functions, configuring specific hyperparameters for VLMs, and managing AWS SageMaker infrastructure requirements. This approach is particularly effective for tasks requiring strict adherence to formats like JSON or high diversity in outputs from vision-based prompts.

주요 기능

• GRPO-Specific Hyperparameter Configuration for VLMs
• Training Stability and Success Metric Monitoring
• 117 GitHub stars
• AWS SageMaker Instance Sizing and Docker Container Setup
• Dataset Structuring for Vision-Language Tasks
• Custom Reward Function Implementation (Formatting, Correctness, Hallucination)

사용 사례

• Improving VLM adherence to complex output schemas through reinforcement learning
• Fine-tuning vision models for high-accuracy tasks where SFT lacks precision
• Extracting structured data from specialized document images with limited training data