🚀 Azure Open-AI Fine-Tuning API Updates: Smarter Control, Seamless Scaling, and Reinforcement Learning
The Azure AI Foundry team continues to push the boundaries of model customization with powerful updates to its fine-tuning capabilities. August brings several new features—Pause & Resume, Cross-Region Model Copying, and Reinforcement Fine Tuning (RFT) with Swagger and API Support—designed to give developers and data scientists greater control, flexibility, and efficiency in managing their fine-tuning workflows.
⏸️ Pause & Resume: Smarter Control Over Fine-Tuning Jobs
Azure AI Foundry extends the ability to pause and resume fine-tuning jobs for non-reasoning models. Earlier this year, we released this feature for reasoning models only. Pause and Resume is especially useful when training metrics aren’t converging or when you need to temporarily halt training without losing progress.
Key Highlights:
- Available via Azure AI Foundry and REST API
- Jobs can be paused only if they’ve completed at least one training step and are in a Running state
- Pausing creates a deployable checkpoint (post safety evaluation) that can be used for inference or resumed later
- Supports Regional and Global Training SKUs
- Pausing action stops the job and billing meters
Foundry UI with Pause and Status highlight for finetuning job
🌍 Copy Models: Seamless Cross-Region Transfer
We are introducing the Copy Models feature, allowing users to transfer checkpointed models across regions and subscriptions within the same tenant—a significant advancement for teams operating in multi-region environments.
Key Highlights:
- Available via REST API only (not supported in UI)
- Models copied to a destination region can be further fine-tuned and deployed independently
- Deleting the source model does not affect the copied model in the destination region
API Endpoints:
Once copied, the checkpoint can be used in a new fine-tuning job using the standard create finetune job API.
🧠 Reinforcement Fine-Tuning (RFT): API and Swagger Ready
RFT is now fully API and Swagger ready, enabling users to fine-tune models using reinforcement learning techniques. Azure OpenAI supports multiple grader types including Score Model Grader, String Check Grader, Text -Similarity Grader and Multi-graders.
Key Highlights:
- Enables model graders within multi-grader
- Supports fail fast by using Validate Grader API which ensures your grader configuration is correct before running a fine-tuning job
- Executes the grader logic on model outputs to generate scores or feedback through Run Grader API
These updates mark a significant step forward in Azure AI Foundry’s commitment to providing enterprise-grade model customization tools. Whether you're optimizing training workflows or scaling models across regions, the new APIs offer the control and agility needed to build smarter AI solutions.
Happy Fine-tuning 😊
Learn More with these Resource
🧠 Get Started with fine-tuning with Azure AI Foundry on Microsoft Learn Docs
▶️ Watch On-Demand: Fine-tuning and distillation with Azure AI Foundry
👩💻 Customize a model with Azure OpenAI in Azure AI Foundry Models
👋 Continue the conversation on Discord
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