GRPO Crash Course: Fine-Tuning DeepSeek for MATH!

I'm happy to share my latest tutorial on Group Relative Policy Optimization (GRPO)! In this video, I break down GRPO in a way that's easy to understand, even if you're new to reinforcement learning. I explain the core concepts using simple language and visuals, aiming for that ELI5 (Explain Like I'm 5) level of clarity. No complex math or jargon here - just the essential ideas behind this powerful technique.
But that's not all! I also dive into a practical demonstration of how to fine-tune a Distill DeepSeek model using the International Mathematical Olympiad (IMO) dataset from Kaggle. I walk you through the entire process, step-by-step, showing you how I improved the model's mathematical reasoning abilities. I cover everything from setting up your environment to evaluating the results. You'll see firsthand how GRPO can be applied to enhance LLMs for complex tasks like solving IMO-level problems.
I believe this video will be incredibly valuable for anyone interested in AI, machine learning, and especially those looking to improve LLMs for mathematical tasks.
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GitHub Repo: github.com/AIA...
DeepSeek Research Paper: arxiv.org/pdf/...
Unsloth Notebooks: docs.unsloth.a...
Kaggle Dataset: www.kaggle.com...
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