DPO vs SFT
DPO (Direct Preference Optimization) and SFT (Supervised Fine-Tuning) both come up in ai & ml conversations and get confused. Here's the plain-English difference, side by side, so you can use each one with confidence.
The key difference: DPO refers to direct preference optimization, while SFT refers to supervised fine-tuning — they describe different things even when they show up in the same sentence.
DPO — Direct Preference Optimization
A training method that aligns models to human preferences using ranked pairs directly, skipping the reward-model step of classic RLHF. Simpler pipeline, fewer moving parts, often comparable results.
SFT — Supervised Fine-Tuning
Training a base model on curated input-output pairs to specialize its behavior. SFT is where most domain models actually get their personality — long before RLHF cleans up the rough edges.
When to use DPO
Reach for "DPO" when the conversation is specifically about direct preference optimization. A training method that aligns models to human preferences using ranked pairs directly, skipping the reward-model step of classic RLHF. Simpler pipeline, fewer moving parts, often comparable results.
When to use SFT
Reach for "SFT" when the conversation is specifically about supervised fine-tuning. Training a base model on curated input-output pairs to specialize its behavior. SFT is where most domain models actually get their personality — long before RLHF cleans up the rough edges.
FAQs
What is the difference between DPO and SFT?
DPO stands for Direct Preference Optimization — A training method that aligns models to human preferences using ranked pairs directly, skipping the reward-model step of classic RLHF. Simpler pipeline, fewer moving parts, often comparable results. SFT stands for Supervised Fine-Tuning — Training a base model on curated input-output pairs to specialize its behavior. SFT is where most domain models actually get their personality — long before RLHF cleans up the rough edges.
Are DPO and SFT the same thing?
No. They're often used in the same conversation because they're related, but they describe different concepts. DPO = Direct Preference Optimization. SFT = Supervised Fine-Tuning.
When should I use DPO vs SFT?
Use DPO when you're specifically referring to direct preference optimization. Use SFT when the topic is supervised fine-tuning.