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Enhance model professionalism: Reinforcement Fine-Tuning (RFT) is designed to optimize models through high-quality task data sets to make the model perform more accurately in complex tasks in a specific domain, thereby elevating the model from "high school level" to "doctoral level expert" capability.
Reduced training data requirements: RFT technology allows developers to fine-tune models using data sets from tens to thousands of high-quality tasks, meaning significant performance gains can be achieved even with limited data (sometimes just a few dozen samples).
Enhanced reasoning: Unlike traditional fine-tuning, reinforcement fine-tuning does not simply make the model "remember the answer", but by training the model to learn to reason in a specific domain, to find the right answer, thereby improving the model's ability to solve similar problems.
Your contribution
i guess i can cooperate with you guys
The text was updated successfully, but these errors were encountered:
Feature request
https://openai.com/form/rft-research-program/
Motivation
Enhance model professionalism: Reinforcement Fine-Tuning (RFT) is designed to optimize models through high-quality task data sets to make the model perform more accurately in complex tasks in a specific domain, thereby elevating the model from "high school level" to "doctoral level expert" capability.
Reduced training data requirements: RFT technology allows developers to fine-tune models using data sets from tens to thousands of high-quality tasks, meaning significant performance gains can be achieved even with limited data (sometimes just a few dozen samples).
Enhanced reasoning: Unlike traditional fine-tuning, reinforcement fine-tuning does not simply make the model "remember the answer", but by training the model to learn to reason in a specific domain, to find the right answer, thereby improving the model's ability to solve similar problems.
Your contribution
i guess i can cooperate with you guys
The text was updated successfully, but these errors were encountered: