Are you tired of relying on Colab or Linux to fine-tune your Large Language Models (LLMs)? Look no further! I’ve created a hands-on guide to help you fine-tune 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 any specialized setup. With this guide, you can experiment with reinforcement learning techniques on your own machine.
## Key Features of the Guide
The guide and 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.
## Get Started
Read the full guide on [Medium](https://pavankunchalapk.medium.com/windows-friendly-grpo-fine-tuning-with-trl-from-zero-to-verifiable-rewards-f28008c89323) and get the code on [GitHub](https://github.com/Pavankunchala/Reinforcement-learning-with-verifable-rewards-Learnings/tree/main/projects/trl-ppo-fine-tuning).
I’m open to any feedback and would love to connect about any opportunities in the LLM / Computer Vision space.