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?
