Microsoft has just announced that o4-mini Reinforcement Fine-Tuning (RFT) is now Generally Available in Azure AI Foundry. This marks a major step forward in how developers can train and optimize AI models, especially for reasoning-heavy use cases.
What Is Reinforcement Fine-Tuning (RFT)?
Traditionally, fine-tuning language models required large datasets of prompt-and-answer pairs (Supervised Fine-Tuning, or SFT). RFT changes this approach completely.
Instead of training on static examples, RFT uses graders—custom rules or logic that reward good outputs and penalize poor ones. This allows models to learn through iterative feedback rather than just memorizing answers.
Key Benefits of RFT with o4-mini
- Data Efficiency: Works with as few as 100 input examples.
- Custom Graders: Define your own evaluation rules in Python code.
- Stronger Reasoning: Ideal for legal, medical, compliance, or decision-making workflows.
- Enterprise Ready: Built directly into Azure AI Foundry with UI and SDK support.
Real-World Example: DraftWise in Legal Tech
One of the early adopters of RFT, DraftWise, used o4-mini RFT to improve AI-powered contract drafting and review. By applying custom logic through graders, DraftWise achieved more accurate, compliant, and useful outputs for legal professionals.
This showcases how RFT can go beyond text generation to deliver practical results in high-stakes industries.
Where and How You Can Use It
- Availability: Now live in East US 2 and Sweden Central regions.
- Access: Deploy via Azure AI Foundry (UI or SDK).
- Learning Resources: Microsoft Learn provides tutorials, including a “Custom Code Grader” demo.
- Community: Join upcoming Model Monday LIVE sessions on YouTube for hands-on training.
SFT vs. RFT: What’s the Difference?
| Feature | Supervised Fine-Tuning (SFT) | Reinforcement Fine-Tuning (RFT) |
|---|---|---|
| Data Size | Thousands of examples | Hundreds of examples |
| Learning Style | Static Q&A pairs | Feedback-driven learning |
| Best For | Predictable outputs | Reasoning-heavy tasks |
| Complexity | Easier to set up | Requires grader design |
If your project involves logic, compliance, or nuanced reasoning, RFT will likely deliver better results than SFT.
Why This Matters
Reinforcement Fine-Tuning on Azure AI Foundry offers:
✅ Faster model iteration cycles
✅ Lower training costs
✅ Smarter outputs aligned with business logic
✅ Flexibility to adapt AI to evolving challenges
This makes o4-mini RFT a game-changer for enterprises and startups alike.
Final Thoughts
With o4-mini RFT now generally available on Azure AI Foundry, developers can push beyond static training methods and create adaptive, reasoning-capable AI models. From law firms to healthcare providers, the potential applications are huge.
If you want to start experimenting with RFT, now is the perfect time to explore Azure AI Foundry and see how reinforcement-based training can level up your AI strategy.
