Knowledge Distillation from Large Reasoning Models to Compact Student Models: A Case Study on the John O Bryan Mathematics Competition

1. 1Build a Chain-of-Thought training corpus using a dual-agent framework
2. 2Fine-tune a compact student model using Low-Rank Adaptation (LoRA)
3. 3Configure the training process on Apple Silicon hardware
4. 4Test the student model on historical problems from the John O'Bryan Mathematics Competition
5. 5Apply knowledge distillation from a large reasoning model to the compact student model
6. 6Evaluate the performance of the student model on math competitions

AI engineers and data scientists can benefit from this knowledge distillation technique to develop more efficient and accurate models, while educators can use these models to improve student performance in math competitions

💡 Knowledge distillation can significantly improve the performance of compact student models on math competitions by leveraging the knowledge of large reasoning models

🤖 Distill knowledge from large reasoning models to compact student models using Chain-of-Thought training & LoRA! 📝