"Reinforcement Fine-Tuning (RFT) is a new method that uses chain-of-thought reasoning and task-specific evaluation to improve model performance in specific application domains."
"Early testers say RFT delivers a 40% performance improvement over standard models without fine-tuning, making it a significant advancement for tailored applications."
"RFT is particularly recommended when decision-making rules are highly specific to an organization and cannot be easily captured through static prompts or traditional training data."
"Supervised Fine-Tuning (SFT) of OpenAI's new GPT-4.1-nano model will be available soon, allowing applications where cost control is critical."
Microsoft has enhanced Azure AI Foundry with a major update to model fine-tuning capabilities, prominently introducing Reinforcement Fine-Tuning (RFT). This innovative approach uses chain-of-thought reasoning to improve model performance significantly, boasting a 40% enhancement over standard models. RFT excels in applications with specific organizational needs, allowing models to adapt to complex decision-making scenarios and unique internal procedures. Additionally, support for Supervised Fine-Tuning of OpenAI's GPT-4.1-nano model will be available shortly, catering to cost-sensitive applications. Overall, these developments underscore Azure's commitment to advancing AI adaptability and performance across diverse industries.
Read at Techzine Global
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