Explain DPO and construct its training data

Q: Explain DPO and construct its training data

This question evaluates understanding of Direct Preference Optimization (DPO) for fine-tuning large language models, assessing conceptual differences from PPO-based RLHF and the competency to design pairwise preference training datasets.

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Question

You are working on a project to fine-tune a large language model (LLM) using Direct Preference Optimization (DPO).

Answer the following:

Conceptual : What is Direct Preference Optimization (DPO) at a high level, and how does it differ conceptually from a standard RLHF pipeline that uses PPO (Proximal Policy Optimization)? Focus on:
- What objective DPO optimizes.
- Why it can avoid training a separate reward model.
- Practical benefits and trade-offs compared with PPO-based RLHF.
Data construction : How would you construct a training dataset suitable for DPO when fine-tuning an LLM? Describe:
- The format of one training example (what fields it contains).
- How to collect or generate the preferred vs dispreferred responses.
- How to handle noisy labels or ties.
- Any preprocessing or filtering you would do to improve data quality.

Assume you start from a base SFT (supervised fine-tuned) model and you have the ability to collect either human preference data or model-generated preference data.

Explain DPO and construct its training data

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Explain DPO and construct its training data

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