OpenAI has launched an expansion of its Reinforcement Fine-Tuning Research Program as part of the ‘12 days of OpenAI’ initiative this December. The program aims to help developers and machine learning engineers create AI models tailored for complex, domain-specific tasks.

The new customization technique allows developers to adjust OpenAI’s models using hundreds to thousands of high-quality tasks. The model’s responses are then graded using reference answers, reinforcing its ability to solve similar problems and improving its accuracy in specific fields.

“We’re expanding our Reinforcement Fine-Tuning Research Program to help developers create expert models that perform well on specialized, complex tasks,” OpenAI shared in a blog post.

What is Reinforcement Fine-Tuning?
This customization technique gives developers the ability to fine-tune OpenAI models for specific domains. By providing a range of high-quality tasks and grading responses, the method strengthens the model’s reasoning skills and accuracy in those particular areas.

Who Should Apply?
OpenAI encourages research institutes, universities, and businesses working on specialized tasks to apply. Fields like law, insurance, healthcare, finance, and engineering have already seen significant improvements using this technique. It is especially effective for tasks with clear, correct answers that experts would universally agree on.

What Does the Program Offer?
Participants in the program will gain access to OpenAI’s Reinforcement Fine-Tuning API in its alpha stage. They will be able to test this customization method for their domain-specific tasks and provide feedback to help improve the API. OpenAI is eager to collaborate with organizations willing to share their datasets to further enhance the models.

Those interested in joining the program can apply through a form, with a limited number of spots available. OpenAI plans to make this Reinforcement Fine-Tuning feature publicly accessible in early 2025.