A fine tuned Mistral-7B through Generative Teaching via synthetic data generated through the proposed AgentInstruct framework, which generates both the prompts and responses, using only raw data sources like text documents and code files as seeds.

A fine tuned Mistral-7B that excels at math problems without external tools, utilizing a high-quality synthetic dataset of 200K problems created through multi-agent collaboration and an iterative learning process that involves practicing problem-solving, receiving feedback, and learning from preference pairs incorporating the model's solutions and feedback.

Introduces Cautious Reasoning for training smaller models to select the most effective solution strategy based on the problem at hand, by crafting data with task-specific system instruction(s) corresponding to the chosen strategy in order to obtain teacher responses for each task and replacing the student’s system instruction with a generic one vacated of details of how to approach the task.

Presents a novel approach that addresses the limitations of instruction tuning by leveraging richer imitation signals, scaling tasks and instructions, and utilizing a teacher assistant to help with progressive learning.