Reinforcement fine-tuning (RFT) lets you improve how models reason by training with graders instead of large labeled datasets. This Build Hour shows you how to set up tasks, design grading functions, and run efficient training loops with just a few hundred examples.
Prashant Mital and Theophile Sautory (Applied AI) cover: - Intro to RFT: optimization, fine-tuning options, RFT benefits - Task setup: prompts, graders, and training and validation data - Live demo: building and running RFT for a classification task - RFT workflow: from dataset selection to evaluating and iterating - Customer spotlight: Accordance uses RFT models for tax and accounting workflows (https://accordance.com/) - Live Q&A
👉 Follow along with the code repo: https://github.com/openai/build-hours 👉 RFT Cookbook: https://cookbook.openai.com/examples/reinforcement_fine_tuning 👉 RFT Use Case Guide: https://platform.openai.com/docs/guides/rft-use-cases 👉 Sign up for upcoming live Build Hours: https://webinar.openai.com/buildhours
