Reinforcement Fine-Tuning LLMs with GRPO
by DeepLearning.AI
Fine-tune LLMs for reasoning with reinforcement learning — no large labeled dataset required.
Overview
Reinforcement Fine-Tuning LLMs with GRPO is a 10-lesson (~1 hour 43 minute) short course from DeepLearning.AI in collaboration with Predibase, taught by Predibase co-founder and CTO Travis Addair and senior ML engineer Arnav Garg. It focuses on Group Relative Policy Optimization (GRPO), a reinforcement-learning method that improves a model's reasoning on tasks with verifiable outcomes (math, coding, games) and can work with fewer than 100 training examples. The course walks through the RFT workflow end to end: how GRPO estimates advantage from groups of sampled completions, how to design reward functions that guide multi-step reasoning, how to use an LLM-as-a-judge for subjective criteria, and how to prevent reward hacking with penalty functions. It uses a Wordle-playing agent as a running case study and shows how to launch reinforcement fine-tuning jobs on Predibase's hosted training platform. Includes 7 code examples and a graded assignment.
At a Glance
- Topic
- Fine-Tuning
- Level
- Intermediate
- Format
- Course
- Cost
- Freemium
- Duration
- ~1 hr 43 min, 10 video lessons, self-paced
- Provider
- DeepLearning.AI
- Hands-on
- Yes — code/exercises
- Certificate
- None
What You’ll Learn
- ✓How Group Relative Policy Optimization (GRPO) works and how it differs from supervised fine-tuning and PPO-style RLHF
- ✓Designing effective reward functions to steer an LLM toward correct multi-step reasoning
- ✓Using an LLM-as-a-judge to score subjective or open-ended outputs
- ✓Preventing reward hacking with penalty functions and understanding GRPO loss components
- ✓Running reinforcement fine-tuning jobs with fewer than 100 examples on the Predibase platform
Highlights
- •Taught by Predibase's co-founder/CTO and lead ML engineer
- •Concrete Wordle-agent case study instead of toy math-only demos
- •Shows RFT works with tiny datasets (<100 examples) on small open-source models
- •Covers reward hacking and penalty design, often skipped in intro RL material
Who It’s For
Best For
- ✓ML/AI engineers fine-tuning open-source LLMs for reasoning tasks
- ✓Developers who lack large labeled datasets but have verifiable success criteria
- ✓Practitioners moving from supervised fine-tuning to reinforcement fine-tuning
Prerequisites
- •Python and basic PyTorch familiarity
- •Understanding of LLM fine-tuning concepts
- •Helpful: prior exposure to reinforcement learning fundamentals
FAQ
What is Reinforcement Fine-Tuning LLMs with GRPO?
A hands-on short course, built by DeepLearning.AI with Predibase, that teaches AI engineers how to use Reinforcement Fine-Tuning (RFT) and the GRPO algorithm to improve LLM reasoning on tasks with verifiable outcomes. Ideal for developers who want to adapt small open-source models without collecting thousands of labeled examples.
Is Reinforcement Fine-Tuning LLMs with GRPO free?
Reinforcement Fine-Tuning LLMs with GRPO offers free content, with paid options for certificates or premium features.
What level is Reinforcement Fine-Tuning LLMs with GRPO for?
Reinforcement Fine-Tuning LLMs with GRPO is aimed at a intermediate audience. Recommended background: Python and basic PyTorch familiarity, Understanding of LLM fine-tuning concepts, Helpful: prior exposure to reinforcement learning fundamentals.
How long does Reinforcement Fine-Tuning LLMs with GRPO take?
Expect roughly ~1 hr 43 min, 10 video lessons, self-paced. Most learners work through it at their own pace.
What will I learn from Reinforcement Fine-Tuning LLMs with GRPO?
You'll learn: How Group Relative Policy Optimization (GRPO) works and how it differs from supervised fine-tuning and PPO-style RLHF; Designing effective reward functions to steer an LLM toward correct multi-step reasoning; Using an LLM-as-a-judge to score subjective or open-ended outputs; Preventing reward hacking with penalty functions and understanding GRPO loss components; Running reinforcement fine-tuning jobs with fewer than 100 examples on the Predibase platform.