On the second day of the “12 Days of OpenAI” we were given the opportunity to see an announcement of enhancement tweaks and a live demo of ChatGPT Pro. Sam Altman was not there, but his team led us through a fascinating preview of what could be an important advance in model customization.

For those who were unable to attend the live briefing, or for those who want to know more in depth what reinforcement fine-tuning entails, here's a quick rundown. Reinforcement fine-tuning (RFT) is an innovative approach that allows developers and machine learning engineers to create AI models tailored to complex domain-specific tasks. In other words, the potential for breakthroughs in science, medicine, finance, and legal discovery is limitless.

Unlike traditional supervised fine tuning, which focuses on training models to reproduce desired outputs, RFT optimizes a model's reasoning ability through lessons and rewards. This advancement represents a major leap forward in AI customization, allowing models to excel in their areas of expertise.

For those of us who are not scientists, this news means that scientific advances in medicine and other industries are closer than we think, with AI helping in ways beyond human understanding. At least, that is the goal of OpenAI.

For the first time, reinforcement learning techniques, previously available only to OpenAI's state-of-the-art models such as the GPT-4o and o1 series, are now available to outside developers. This democratization of advanced AI training methods paves the way for highly specialized AI solutions.

Developers and organizations can now create expert-level models without the need for extensive reinforcement learning expertise; RFT's focus on reasoning and problem solving will prove particularly relevant in areas where accuracy and expertise are required.

Applications range from facilitating scientific discovery to streamlining complex legal workflows and may represent a paradigm shift in the application of AI to real-world challenges.

One of the distinguishing features of RFT is its developer-friendly interface. The user only needs to provide a dataset and a grader, and the reinforcement and learning process is handled by OpenAI. This simplicity lowers the barrier to entry and allows a wider range of developers and organizations to harness the power of RFT.

Yesterday's o1 preview and today's discussion of reinforcement tweaks were fascinating. We are just beginning the countdown and there is much more to come from Altman and his team.

The event wraps up for the weekend, but we'll have more exciting news next week: will we get more from OpenAI's Canvas; will there be a project-type upgrade that will allow ChatGPT to be used in groups? Will there be a project type upgrade that will allow ChatGPT to be used in groups? Stay tuned!