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grpo-finetuning

Automate GRPO-based fine-tuning of vision-language models with Python, PyTorch, and TRL.

392|152|Updated Jul 9, 2025
Search Tags:#fine-tuning#model-training#machine-learning#reinforcement-learning#vision-language#grpo#SageMaker
One-click install
npx skills add https://github.com/aws-solutions-library-samples/guidance-for-claude-code-with-amazon-bedrock --skill grpo-finetuning
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: grpo-finetuning
Source: https://github.com/aws-solutions-library-samples/guidance-for-claude-code-with-amazon-bedrock/tree/main/assets/claude-code-plugins/plugins/ml-training/skills/grpo-finetuning
Command: npx skills add https://github.com/aws-solutions-library-samples/guidance-for-claude-code-with-amazon-bedrock --skill grpo-finetuning

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill addresses data-efficient fine-tuning of vision-language models when labeled data is scarce, enabling stronger performance with GRPO.

Core Features & Use Cases

  • Reward-based fine-tuning: Uses multiple completions with reward functions to optimize the policy.
  • Data-efficient training: Effective on small datasets (<1000 examples) to improve performance.
  • Use Case: When you have limited labeled data and need robust vision-language alignment, apply GRPO fine-tuning to improve model quality with minimal data.

Quick Start

Run the GRPO fine-tuning workflow with your dataset and configured reward functions to start training a vision-language model.

Frequently Asked Questions about grpo-finetuning

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fine-tune a vision-language model with limited labeled data?▼

GRPO fine-tuning improves vision-language models on small datasets under 1000 examples by using reward functions to optimize the policy, achieving stronger performance with minimal data.

Can I use GRPO for reinforcement learning on small datasets in SageMaker?▼

GRPO fine-tuning in SageMaker supports data-efficient reinforcement learning on small datasets by configuring reward functions and training setups with Python, PyTorch, and TRL.

What is GRPO-based training for vision-language tasks?▼

GRPO-based training for vision-language tasks is a reward-based fine-tuning method that generates multiple completions and applies reward functions to optimize the policy model.

How do I set up reward functions for GRPO model training?▼

You set up GRPO model training by configuring custom reward functions and dataset parameters within the Python, PyTorch, and TRL framework to automate the fine-tuning workflow.

Does GRPO fine-tuning work for vision-language alignment when labels are scarce?▼

GRPO fine-tuning works for vision-language alignment when labels are scarce by applying reward-based optimization to improve model quality with fewer than 1000 labeled examples.

What are the limitations of using GRPO for vision-language model training?▼

GRPO for vision-language model training is limited to scenarios with small datasets and requires configuring appropriate reward functions, relying on Python, PyTorch, and TRL for implementation.

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