Training DeepSeek R1 1.5B Model for Enhanced Mathematical Reasoning Through Coldstart Method

Explore a 42-minute technical video demonstrating how to enhance mathematical reasoning capabilities in smaller language models through the DeepSeek R1 coldstart method. Learn the process of structuring and generating chain-of-thought prompts based on the DeepSeek R1 paper, and discover how to create synthetic training data using a custom mathematics compiler. Follow along with the complete fine-tuning pipeline that enables a 1.5B parameter model to achieve superior mathematical problem-solving performance compared to larger models. Dive into key concepts including think tags, synthetic chain-of-thought generation, natural language translation, self-reflection mechanisms, and practical implementation using Qwen2.5-1.5B. Access companion GitHub repositories for the math compiler and verifiers while following a detailed walkthrough from initial setup through testing and final model evaluation.