A Guide to GRPO Fine-Tuning on Windows Using the TRL Library

Hey everyone,

I wrote a hands-on guide for fine-tuning LLMs with GRPO (Group-Relative PPO) locally on Windows, using Hugging Face's TRL library. My goal was to create a practical workflow that doesn't require Colab or Linux.

The guide and the accompanying script focus on:

• A TRL-based implementation that runs on consumer GPUs (with LoRA and optional 4-bit quantization).
• A verifiable reward system that uses numeric, format, and boilerplate checks to create a more reliable training signal.
• Automatic data mapping for most Hugging Face datasets to simplify preprocessing.
• Practical troubleshooting and configuration notes for local setups.

This is for anyone looking to experiment with reinforcement learning techniques on their own machine.

Read the blog post: https://pavankunchalapk.medium.com/windows-friendly-grpo-fine-tuning-with-trl-from-zero-to-verifiable-rewards-f28008c89323

Get the code: Reinforcement-learning-with-verifable-rewards-Learnings/projects/trl-ppo-fine-tuning at main · Pavankunchala/Reinforcement-learning-with-verifable-rewards-Learnings

I'm open to any feedback. Thanks!

P.S. I'm currently looking for my next role in the LLM / Computer Vision space and would love to connect about any opportunities

Portfolio: Pavan Kunchala - AI Engineer & Full-Stack Developer.