Certification
Certification: DeepSeek R1 Architecture, GRPO & KL Divergence Expertise
Upgrade your CV with proven expertise in DeepSeek R1 Architecture, GRPO, and KL Divergence. This certification validates advanced AI skills, giving you an edge in technical roles and modern data science environments.
The exam
Take the certification exam
Multiple-choice questions about the course. Pass with 70% or more and your certificate is issued at once, with a public page and an "Add to LinkedIn" button.
What the exam covers
2 questions from each of the 15 chapters of the course
20 multiple-choice questions, drawn fresh for every attempt. Pass with 70% or more.
- 01R1 overview and the R1-Zero path5:32
- 02Reinforcement learning setup3:04
- 03GRPO4:28
- 04R1-Zero results3:49
- 05Cold-start SFT and consistency4:13
- 06Neural reward models and distillation5:49
- 07PPO vs GRPO3:30
- 08PPO formula3:00
- 09GRPO formula and pseudo-code5:31
- 10GRPO trainer code10:28
- 11KL divergence in GRPO vs PPO1:56
- 12KL divergence refresher4:12
- 13Monte Carlo KL and k14:29
- 14k2 and k3 estimators4:43
- 15Benchmarking and conclusion2:59
The Certification: DeepSeek R1 Architecture, GRPO & KL Divergence Expertise recognizes advanced proficiency in the core principles and methodologies behind DeepSeek R1 and its associated optimization techniques. By mastering these concepts, you gain a competitive advantage through improved decision-making, adaptability, and future-proof skills in the evolving AI landscape. Enroll now to elevate your expertise and unlock greater career potential.
This certification covers the following topics:
- Understanding DeepSeek R1
- Reinforcement Learning in DeepSeek R1
- Group Relative Policy Optimization (GRPO)
- KL Divergence for Model Stability
- Distillation for Smaller, Efficient Reasoning Models
- DeepSeek V3 Base as the Foundation
- DEC R1-0 Achieves Near OAI Level Reasoning
- GRPO Loss Function in TRL
- KL Divergence Estimator K3
- Customizable Reward Functions
- What is Group Relative Policy Optimization (GRPO) and how does it differ from traditional Reinforcement Learning methods like PPO?
- What is the purpose of the Knowledge Divergence (KL Divergence) penalty term used in GRPO?
- How was DeepSeek R1 distilled into smaller, more accessible models?
- Briefly describe the two main components of the reasoning-oriented reinforcement learning process used to train DeepSeek R1.
- What are the benefits of using Group Relative Policy Optimization (GRPO) over traditional methods like PPO?