Building a DeepSeek R1-Style Reasoning LLM with GRPO Fine-Tuning

Learn how to create a Deepseek R1-style reasoning Language Learning Model through an experimental tutorial focused on GRPO-based fine-tuning. Explore the reward functions used in building a Math Reasoner, all implementable within a free Google Colab notebook. Dive into the technical aspects of training, including access to failed training examples through Weights and Biases Dashboard, and examine both standard and A100 GPU-requiring implementations. Follow along with provided Colab notebooks to understand the intricacies of transforming any LLM into a mathematical reasoning powerhouse, while acknowledging the experimental nature of the process and potential variations in success rates.