and
technology preview. This is a brand-new model customization technology that can help companies build expert-level models in specific complex fields such as programming, scientific research, or finance.
What is Reinforcement Fine-Tuning?
Reinforcement Fine-Tuning is a new model customization method that allows developers to customize models with dozens to thousands of high-quality tasks and score model responses using provided reference answers. This technology can reinforce the model's reasoning approach to solving similar problems, thereby improving its accuracy in specific domain tasks.
Applicable Audience
OpenAI plans to officially release the Reinforcement Fine-Tuning feature to the public in early 2025. However, you can currently apply for the whitelist.
The following organizations can apply to join this program:
Research institutions and universities: Scientific research teams with cutting-edge technology and complex tasks. Enterprises: Especially those executing expert-led, highly complex, and narrowly scoped tasks. Reinforcement Fine-Tuning excels in the following areas: Law, insurance, healthcare: Involves precise scenarios with clear answers. Finance, engineering: Task results need to meet high standards widely recognized by experts.
Clearly, our company is not the target user. We will wait for the API to be open next year.
The role of Reinforcement Fine-Tuning in business
GPT (Generative Pre-trained Transformer) is a pre-trained generative model, and its pre-training stage can be seen as the general education phase of a "high school student." During this phase, the model acquires broad foundational skills, such as relatively universal skills like listening, speaking, reading, and writing.
However, when the model enters the "college stage," it needs to specialize in certain professional fields. For example, in-depth capabilities in computer science, literature, history, architecture, etc., require further specialized training. At this point, Reinforcement Fine-Tuning plays a key role.
Why do we need Reinforcement Fine-Tuning?
The advantage of pre-trained models lies in their universality, but in specific commercial scenarios, relying solely on general capabilities may not meet the requirements. Reinforcement Fine-Tuning can transform the model into an "expert" in a particular field, enhancing its performance in specific tasks.
It is worth noting that focusing on training capabilities in certain fields may lead to a decline in capabilities in other areas. For example, after the model improves its performance in financial analysis, its performance in literary creation tasks may slightly decrease. However, from the perspective of commercial value, high-level capabilities in specific fields clearly better meet industry needs.
The last part is my personal thoughts, I might be wrong. Just take a look.