That's going to be ideal as your chosen dataset. And you can always generate a rejected or lower quality dataset just by putting the same prompts into a language model and seeing what generic response it comes back with. So this approach here, called ORPO, or Odds Ratio Preference Optimization, it allows you to do both SFT, supervised fine-tuning, and preference fine-tuning at once.