SFT vs. DPO vs. RLVR: How the Three LLM Customization Methods Differ in Supervision Signals
blaizedsouza · x · 2026-10-14
A reshared explainer by Avi Chawla breaks down how three LLM customization methods learn from different supervision signals, using a math problem with a known answer as the running example:
- SFT: needs a prompt plus an ideal completion; the model learns to reproduce it. Works when high-quality responses already exist; the cost is writing and validating one per prompt.
- DPO: needs a preferred and a rejected response for the same prompt; training boosts the preferred one without a separate reward model. Useful when comparing two answers is easier than writing one from scratch.
- RLVR: needs a prompt, a reference answer, and a programmatic verifier; the model samples multiple solutions and the verifier scores each. Ideal when answers can be checked automatically but ideal responses are expensive to author.
The linked article also includes a full RLVR tutorial on SageMaker AI.
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