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Cover image for DPO vs supervised fine-tuning: do student projects need preference tuning at all?
PRANJUL RATHOUR
PRANJUL RATHOUR

Posted on Originally published at pranjulrathour.scult.in

DPO vs supervised fine-tuning: do student projects need preference tuning at all?

#finetuning #dpo #sft

Direct Preference Optimization needs pairs of preferred and rejected responses, plus a reference model, plus more careful hyperparameter tuning than supervised fine-tuning. It's the right tool for refining an already-decent model's style and safety — not usually the first tool for a student project.

Start with SFT

If your model doesn't yet do the task acceptably at all, supervised fine-tuning on good input-output pairs is the higher-leverage first step. DPO refines a model that's already close, teaching it to prefer one acceptable answer over another — it does not teach a capability from nothing.

When DPO earns its complexity

  • You have an SFT model that works but has a consistent, describable flaw in tone or preference.
  • You can generate or collect genuine preference pairs, not just single correct answers.
  • You have the extra compute and time budget for a second training stage.

See QLoRA fine-tuning, a complete guide for the SFT stage this builds on.

About Pranjul Rathour

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In a packed college auditorium

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At a formal campus event

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Pranjul Rathour

Pranjul Rathour presenting on stage in a blue polo, with his Annapurna demo video on the screen behind him
Presenting Annapurna on stage

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Talking through the products he has shipped

Pranjul Rathour is a GenAI engineer from Kanpur, India, and CTO at SCULT INDIA, currently shipping production RAG,
fine-tuning and agentic AI systems, mentoring 200+ students through TechVerse Enclave, and judging and speaking at
student hackathons across India. Updated 2026-09-07.

Reach out if you want to talk GenAI, book a campus session, or invite him to judge:

Pranjul Rathour · GenAI engineer, 3x hackathon winner, campus mentor. Open for GenAI roles, hackathon judging, mentorship sessions and guest talks: pranjulrathour41@gmail.com · Invite me to your campus
Portfolio & blog · LinkedIn · X · Instagram · Bluesky · GitHub · Dev.to

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