Understanding DeepSeek R1 Reward Modeling and Verifiers for AI Training

Learn how to implement DeepSeek and O1's rule-based AI reward modeling using verifiers in a comprehensive 45-minute video tutorial. Master the creation of various verifiers including format, accuracy, boxed, and limerick types to shape AI-generated outputs. Explore different sampling strategies like greedy vs. top-p sampling, and discover how to generate custom verifier datasets. Follow along to understand the process of collecting prompts from a DeepSeek teacher model and implementing the same techniques used in the DeepSeek paper to evoke long chains of thought. Gain practical experience in fine-tuning models through SFT training on collected prompts and evaluating their performance and chain-of-thought quality. Access companion GitHub repositories for fine-tuning, verifiers, and math synthetic data generation to create your own reasoning-based model locally. Perfect for both AI enthusiasts and experienced practitioners looking to enhance their understanding of AI reward strategies and verifier implementation.