Member-only story
Introduction
In the ever-evolving landscape of artificial intelligence, the post-training phase of foundation models plays a crucial role in determining their capabilities. The research paper “SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training” by Tianzhe Chu et al. provides a groundbreaking comparative analysis of Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) in enhancing model performance. This study explores the fundamental differences between these approaches, particularly focusing on memorization vs. generalization in textual and visual environments.
Understanding SFT and RL in Foundation Models
Supervised Fine-Tuning (SFT)
SFT is a common post-training technique where a model is trained on labeled data to refine its outputs. While it is effective in improving performance on specific tasks, the study finds that SFT tends to memorize the training data rather than learning generalizable patterns.
Reinforcement Learning (RL)
Reinforcement Learning, particularly RL with outcome-based rewards, enables a model to learn through iterative feedback. The study shows that RL is superior in generalization, allowing models to perform well on unseen data, even across different rule sets and environments.
