GRPO Fine-Tuning for Vision Models FAQs

Question 1

What are the infrastructure requirements for GRPO?

Accepted Answer

GRPO requires significantly more VRAM than SFT because it generates multiple completions per prompt. For a 7B model, an ml.g5.4xlarge or ml.p4d.24xlarge instance is typically required.

Question 2

How much longer does GRPO training take compared to SFT?

Accepted Answer

GRPO is generally slower due to the generation overhead. Each training step can take 150-200 seconds compared to 30-50 seconds for standard Supervised Fine-Tuning.

Question 3

When should I choose GRPO over traditional SFT?

Accepted Answer

GRPO is recommended when your dataset is small (less than 1,000 examples), you need diverse outputs, or you have very clear correctness criteria such as specific JSON formatting.

Question 4

Which models are compatible with this GRPO implementation?

Accepted Answer

This skill focuses on vision-language models like Qwen2-VL, but the principles and reward structures can be adapted for most modern VLMs supported by the Unsloth or TRL libraries.

Question 5

How do reward functions work in this skill?

Accepted Answer

Reward functions score model completions based on criteria like JSON validity or field accuracy. The GRPO trainer uses these scores to optimize the policy to favor higher-reward outputs.

GRPO Fine-Tuning for Vision Models

Key Features

Use Cases

GRPO Fine-Tuning for Vision Models

Key Features

Use Cases