High Fine-tuning vs RAG Question 170 of 223

How do SFT, DPO, and RLHF differ as alignment methods?

GenAI / LLM · Speak this in 60–90 seconds · Faridabad & Delhi NCR

ExamplesData + labels

TrainModel learns

New inputPredicted label

SFT clones demonstration answers via supervised loss.

LLMs use tokens (not full words) because it helps them:

This is a process

question about Fine-tuning vs RAG.

Panels listen for order,

trade-offs, and what you would actually do on a GenAI / LLM project - not buzzwords.

Stable token IDs

Each piece maps to a number the network can learn.

Fits the model

Fixed pieces are what transformers expect as input.

Before you speak the answer, walk the interviewer through these steps:

1
SFT clones demonstration answers
via supervised loss.
2
DPO trains on preferred
versus rejected pairs without a separate reward model.
3
Classic RLHF fits a
reward model then PPO-updates the policy, which is more complex and can chase reward hacks.
4
Give an example
One tiny concrete case you can say aloud.
5
Common mistake
What juniors usually get wrong.
6
Close
When you pick this over the alternative.

Here's a short line you can speak, broken into clear beats:

Say this line

“DPO trains on preferred versus rejected pairs without a separate reward model.”

Break into beats

DPOtrainsonpreferredversusrejected

Speaking order

2987408337471632900

Note: Adapt this scaffold to your own project — keep it under 60–90 seconds.

SFT clones demonstration answers via supervised loss. DPO trains on preferred versus rejected pairs without a separate reward model.