For Day 2 of their “12 Days of OpenAI” year-end festival, OpenAI is expanding its Reinforcement Fine-Tuning Research Program. This program supports developers and machine learning engineers in creating models that handle specific sets of complex and specialised tasks. Reinforcement Fine-Tuning is a method that allows developers to adjust models by providing high-quality task datasets. Using reference answers, developers evaluate the model’s responses, helping it learn how to solve similar problems more accurately in a specific domain.

Sam Altman, OpenAI’s CEO, wrote on X (formerly Twitter), “Today we are announcing Reinforcement Fine-Tuning, which makes it really easy to create expert models in specific domains with very little training data.”

OpenAI has encouraged research institutes, universities, and organisations that perform specialised tasks that require expert input to join. Fields like Law, Insurance, Healthcare, Finance, and Engineering have shown promising results. Reinforcement Fine-Tuning is particularly effective for tasks where there is a clear and agreed-upon correct answer.

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Participants will gain early access to the alpha version of the Reinforcement Fine-Tuning API. They can test this approach on their specific tasks and provide feedback to improve the API before its public launch. Organisations willing to share datasets will contribute to enhancing model performance further.

Day 1 saw the introduction of a $200 subscription-based ChatGPT Pro, that provides unlimited access to OpenAI o1, o1-mini, GPT-4o, and Advanced Voice.

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