Reinforcement Fine-Tuning (RFT):
- Concept: Unlike standard fine-tuning, RFT focuses on teaching models to reason in new ways rather than mimicking patterns from inputs.
- Methodology: Models are trained using RL to reinforce correct reasoning paths and penalize incorrect ones, allowing for significant learning even with a few dozen examples.
- Applications: Ideal for domains requiring deep expertise, such as legal, finance, healthcare, and scientific research.

Comparison to Standard Fine-Tuning:
- Standard fine-tuning modifies model output styles or formats.
- RFT empowers models to tackle complex reasoning tasks by generalizing knowledge to new scenarios.

Training and Validation Process
Data Preparation:
- Training and validation datasets were JSONL files with structured examples.
- Validation data was distinct from training data to ensure the model learned to generalize rather than memorize.
Grading Mechanism:
- Introduced a “grader” to evaluate model outputs against correct answers.
- Scores ranged from 0 (completely incorrect) to 1 (fully correct), with partial credit for near-accurate predictions.
Fine-Tuning Workflow:
- Uploaded training data and selected hyperparameters.
- Utilized OpenAI’s distributed infrastructure to train models.

Results and Insights
Performance Metrics:
- Top-1 Accuracy: Frequency of the correct answer being the top prediction.
- Top-5 Accuracy: Correct answer appearing in the top 5 predictions.
- O1 Mini improved from 17% (baseline) to 31% (fine-tuned model) in top-1 accuracy.
Validation and Generalization:
- Fine-tuned models showed significant improvement in generalization, evidenced by better performance on unseen validation data.
Scientific Impact:
- Enhanced reasoning capabilities for rare disease diagnosis.
- Potential to integrate AI models with traditional bioinformatics tools for improved healthcare outcomes.