Beyond Scaling Law: A Data-Efficient Distillation Framework for Reasoning

1. 1Apply distillation techniques to large language models to improve reasoning capabilities
2. 2Use reinforcement learning and supervised fine-tuning to enhance model performance
3. 3Configure multistage training to combine different learning methods
4. 4Test the framework on tasks such as algorithmic coding and mathematical problem-solving
5. 5Run experiments to evaluate the effectiveness of the data-efficient distillation framework

AI engineers and researchers on a team can benefit from this framework to develop more efficient and effective language models, while data scientists can apply this knowledge to improve model performance

💡 Data-efficient distillation can improve reasoning capabilities in LLMs without requiring large amounts of data

💡 Improve LLM reasoning with data-efficient distillation!