Mastering Model Graders for Reinforcement Fine-Tuning

Discover how to apply reinforcement fine-tuning (RFT) to OpenAI's o4-mini model using a medical dataset for outcome prediction from transcripts. This guide equips experienced developers with step-by-step instructions to benchmark, grade, train, and deploy fine-tuned reasoning models for research purposes. Unlock sharper decision-making and enhanced context interpretation.

This guide targets developers and ML practitioners familiar with OpenAI’s APIs, possessing a foundational grasp of reinforcement fine-tuning (RFT), and aiming to deploy fine-tuned models for research or suitable applications. OpenAI’s services are not designed for personalized medical treatment or diagnosis and are governed by our relevant terms.

Reinforcement fine-tuning (RFT) enhances reasoning models by layering reinforcement learning on top, enabling them to probe the solution space and strengthen strategies yielding superior rewards. RFT refines the model's decision precision and boosts its context comprehension.

This guide details applying RFT to OpenAI's o4-mini reasoning model via a life sciences task: forecasting outcomes from doctor-patient transcripts and descriptions, a vital evaluation in numerous health research efforts. We'll employ a subset of the medical-o1-verifiable-problem dataset. You'll gain the critical steps needed to execute RFT jobs for your specific scenarios.

Here’s what we’ll cover:

• 1. Setup

• 2. Gathering the dataset

• 3. Benchmarking the base model

• 4. Defining your grader

• 5. Training

• 6. Using your fine-tuned model