Are you tired of relying on Colab or Linux to fine-tune Large Language Models (LLMs)? Look no further! In this post, I’ll walk you through a hands-on guide to fine-tuning LLMs with Group-Relative PPO (GRPO) on Windows using Hugging Face’s TRL library.
Why is this important? Well, having a local setup can be a game-changer for researchers and developers who want to experiment with reinforcement learning techniques without relying on cloud services.
## What You’ll Learn
This guide focuses on four key aspects:
* **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.
## Get Started
If you’re new to GRPO or reinforcement learning, don’t worry! This guide is designed to take you from zero to verifiable rewards. You’ll learn how to set up your environment, prepare your data, and fine-tune your LLM using GRPO and TRL.
**Read the full guide:** https://pavankunchalapk.medium.com/windows-friendly-grpo-fine-tuning-with-trl-from-zero-to-verifiable-rewards-f28008c89323
**Get the code:** https://github.com/Pavankunchala/Reinforcement-learning-with-verifable-rewards-Learnings/tree/main/projects/trl-ppo-fine-tuning
Happy fine-tuning!