Making Expert Reasoning Learnable with Self-Distillation

1. 1Apply self-distillation techniques to LLMs to learn from expert human solutions
2. 2Configure the model to extract valid training signals from the expert solutions
3. 3Build a dataset of high-quality expert human solutions for the model to learn from
4. 4Run experiments to evaluate the effectiveness of self-distillation in improving the model's reasoning capabilities
5. 5Test the model on intractable problems to assess its performance

AI engineers and researchers can benefit from this approach to enhance the performance of their LLMs, while data scientists can leverage the improved models for various applications

💡 Self-distillation allows LLMs to learn from expert human solutions, overcoming the limitations of current models and enabling them to tackle intractable problems

🤖 Improve LLMs' reasoning with self-distillation & expert human solutions! 💡