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SFT vs RFT: Choosing a Fine-Tuning Objective

The decision framework for choosing between supervised fine-tuning (static labeled data) and reinforcement fine-tuning (online reward-based exploration via GRPO) — including the full labeled-data/verifiability decision tree.

SFT vs. RFT — Decision Tree

Walk the tree from the root: whether you have labeled data, whether the task is automatically verifiable, and how much data you have all determine which fine-tuning objective fits.

SFT vs. RFT — Decision Tree

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NoYesNoYesLargeTinyYesNoLabeled data?Task verifiable?How much data?Use RLHFUse RFTUse SFTReasoning helps?Use RFTUse SFT
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▶📚 Prerequisites(1)

🎓 Learning objectives

  • •Contrast the SFT process (static labeled data, weight matching) with the RFT process (online reward exploration via GRPO)
  • •Apply the full decision tree: labeled data? verifiable task? reasoning-helps? to choose SFT, RFT, or RLHF
  • •Explain why RFT is well suited to reasoning-heavy tasks like math and logic
  • •Identify when a tiny dataset should still use SFT instead of RFT

SFT vs RFT: Choosing a Fine-Tuning Objective is a Pro topic

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📂 Subtopics

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Supervised Fine-Tuning (SFT): Static Labels, Matching Completions

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Reinforcement Fine-Tuning (RFT): Reward Signals Instead of Static Labels

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When to Use SFT vs RFT: The Book's Decision Tree

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Practical Tradeoffs: Data, Compute, Stability and Real Examples

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