Reinforcement Fine-Tuning LLMs with GRPO
by DeepLearning.AI
Teach a small model to reason with under 100 examples — and derive the GRPO loss line by line.
Overview
Built with Predibase and taught by its co-founder and CTO Travis Addair together with senior machine learning engineer Arnav Garg, this 1-hour-43-minute course teaches reinforcement fine-tuning using Group Relative Policy Optimization. The through-line is a single concrete task — teaching a small open model to play Wordle — chosen because the outcome is verifiable, which is the condition under which GRPO works well. Ten lessons run Introduction (3m), Introduction to reinforcement learning (7m), Benefits of reinforcement finetuning (4m), Can a large language model master Wordle (10m), Reward functions (10m), Reward functions with LLM as a judge (12m), Reward hacking (7m), Calculating loss in GRPO (18m), Putting it all together: Training Wordle (8m) and Conclusion (1m), plus a graded quiz and a downloadable code appendix. The longest lesson is the loss derivation, which decomposes GRPO into its four components: token probability distribution ratios, advantages, clipping and KL divergence. The practical argument the course makes is that reinforcement fine-tuning can work with fewer than 100 training examples where supervised fine-tuning would need thousands, because the model develops its own strategy for completing the task rather than imitating pre-existing labelled examples. Reward hacking gets its own dedicated lesson, with penalty functions as the mitigation. Training jobs are launched against Predibase's hosted reinforcement fine-tuning service, so no local GPU cluster is required to follow along.
At a Glance
- Topic
- Fine-Tuning
- Level
- Intermediate
- Format
- Course
- Cost
- Freemium
- Duration
- ~1 hour 43 minutes across 10 lessons, self-paced
- Provider
- DeepLearning.AI
- Hands-on
- Yes — code/exercises
- Certificate
- Available
What You’ll Learn
- ✓Decide when reinforcement fine-tuning beats supervised fine-tuning on labelled example data
- ✓Decompose the GRPO loss into probability ratios, advantages, clipping and KL divergence
- ✓Design reward functions for tasks with verifiable outcomes such as Wordle
- ✓Use an LLM-as-a-judge reward when the target output is subjective
- ✓Detect reward hacking and write penalty functions that suppress it
- ✓Convert raw rewards into group-relative advantages across sampled completions
- ✓Launch and monitor a hosted reinforcement fine-tuning job on Predibase
Highlights
- •Taught by the CTO of the company that built a commercial reinforcement fine-tuning platform, not an outside instructor
- •18 full minutes spent on the GRPO loss itself, where most tutorials hand-wave the maths
- •Reward hacking gets a dedicated lesson rather than a footnote at the end
- •Targets the under-100-example regime, so the technique is reachable without a labelling budget
- •A community GitHub repository mirrors the lesson notebooks for offline reference
Who It’s For
Best For
- ✓ML engineers adapting small open models to reasoning or agentic tasks
- ✓Teams without a large labelled dataset who still need task-specific accuracy
- ✓Practitioners who want a concrete, runnable grounding in modern RL post-training
Prerequisites
- •Python, plus prior experience fine-tuning a transformer model
- •Basic reinforcement learning vocabulary: policy, reward, advantage
- •Familiarity with LLM evaluation and prompt design
FAQ
What is Reinforcement Fine-Tuning LLMs with GRPO?
A short course built with Predibase that teaches reinforcement fine-tuning using Group Relative Policy Optimization, worked end to end on a single verifiable task: teaching a small open model to play Wordle. It covers reward design, LLM-as-a-judge scoring, reward hacking and an 18-minute derivation of the GRPO loss itself.
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, plus prior experience fine-tuning a transformer model, Basic reinforcement learning vocabulary: policy, reward, advantage, Familiarity with LLM evaluation and prompt design.
How long does Reinforcement Fine-Tuning LLMs with GRPO take?
Expect roughly ~1 hour 43 minutes across 10 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: Decide when reinforcement fine-tuning beats supervised fine-tuning on labelled example data; Decompose the GRPO loss into probability ratios, advantages, clipping and KL divergence; Design reward functions for tasks with verifiable outcomes such as Wordle; Use an LLM-as-a-judge reward when the target output is subjective; Detect reward hacking and write penalty functions that suppress it; Convert raw rewards into group-relative advantages across sampled completions; Launch and monitor a hosted reinforcement fine-tuning job on Predibase.
Topics
Sources
This page was written from 2 sources, 1 on domains other than deeplearning.ai.