Welcome to the Reinforcement Fine-Tuning LLMs with GRPO course, built in partnership with Predibase. This course offers a deep dive into how Group Relative Policy Optimization (GRPO) can be used to fine-tune LLMs for enhanced reasoning—even with small datasets.
Fine-tune language models with reinforcement learning (RL) techniques that go beyond traditional supervised methods. Learn how to use GRPO, a powerful and efficient RL algorithm, to guide LLMs in tasks requiring multi-step reasoning like coding and math.
- 🚦 Reinforcement Fine-Tuning (RFT): Understand when and why to use RFT over supervised fine-tuning—especially for strategy-based tasks.
- 🧮 GRPO Algorithm: Explore how GRPO leverages programmable reward functions and statistical techniques (advantage estimation, clipping, KL-penalty) to steer model behavior.
- 🧠 Reward Design: Craft reward functions to align model outputs with task goals and avoid reward hacking using penalty functions.
- 🤖 LLM-as-a-Judge: Learn how to evaluate subjective tasks using a language model to rate quality without human annotators.
- 🎮 Wordle with GRPO: See how the Wordle game is framed as a reinforcement learning task for LLMs to develop planning and feedback adaptation.
- 🚀 Train on Predibase: Learn how to launch and manage fine-tuning jobs using Predibase’s hosted RL infrastructure.
- 🧠 RFT enables strategic learning without requiring millions of examples.
- 📊 GRPO is more scalable and deterministic than traditional RLHF.
- ⚙️ Token-level control with KL-divergence and clipping enables stable training.
- ✅ LLM-as-a-Judge offers scalable evaluation for subjective tasks.
- 🛠️ Predibase platform support simplifies fine-tuning pipelines.
Travis Addair
Co-Founder and CTO at Predibase
Arnav Garg
Senior Machine Learning Engineer at Predibase
Together, they bring deep expertise in large-scale model training, open-source infrastructure, and reinforcement learning applications in production.
🚀 Get started with reinforcement fine-tuning today:
📚 deeplearning.ai