Supervised Fine Tuning on Curated Data Is Reinforcement Learning

Summary

The article argues that supervised fine-tuning (SFT) on carefully curated datasets functions similarly to reinforcement learning (RL), as both approaches optimize models based on human preferences or feedback. This challenges the traditional distinction between SFT and RLHF (Reinforcement Learning from Human Feedback), suggesting that the line between them is more blurred than commonly thought. The implication is that advances in SFT could directly impact RL methods and vice versa, influencing how AI systems are trained for alignment and safety.