OpenAI Series #2: Enhanced Fine-Tuning – Train Your Expert Model with Minimal Samples
Discover OpenAI's Reinforcement Fine-Tuning (RFT): Train expert models with minimal samples for advanced reasoning in law, finance, research, and more.
Reinforcement Fine-Tuning Enables Easy Creation of Expert Models with Advanced Reasoning Capabilities
Have you processed yesterday’s news about o1 and the $200-per-month o1-pro? Let’s give credit where it’s due and criticize where necessary, but one thing is clear—OpenAI understands marketing. Their 12-day consecutive release strategy has certainly captured attention.
Now, OpenAI’s 12-day plan has entered Day 2. At 2 AM, they launched a product more appealing to developers and researchers: Reinforcement Fine-Tuning (RFT).
The announcement involved four contributors: Mark Chen, VP of Research at OpenAI; John Allard and Julie Wang, both OpenAI engineers; and Justin Reese, a researcher in environmental genomics and systems biology at Berkeley Lab.
Mark Chen stated, “Reinforcement Fine-Tuning allows you to turn your golden dataset into a unique product, empowering you to bring our magical capabilities to your users and customers.” However, the product won’t be fully available until next year.