Explain Supervised Fine-Tuning, RLHF, and DPO
This tests your understanding of modern LLM alignment techniques. A strong answer explains that Supervised Fine-Tuning (SFT) teaches the model a task via imitation, while RLHF and DPO align it with human preferences. RLHF uses a reward model and reinforcement learning, whereas DPO is a simpler, direct optimization method. The key red flag is conflating these distinct stages or failing to explain the 'reward model' step in RLHF.
This question probes your detailed knowledge of the LLM training and alignment pipeline. A great answer differentiates the three methods by their goal, data, and mechanism. Start with Supervised Fine-Tuning (SFT), which uses curated prompt-response pairs to teach the model specific skills. Then, explain Reinforcement Learning from Human Feedback (RLHF) as a preference-tuning step that involves training a separate reward model on human rankings and then using RL to optimize the LLM. Finally, introduce Direct Preference Optimization (DPO) as a more modern, stable alternative that bypasses the reward model and RL complexity by directly optimizing on preference pairs. A common mistake is failing to articulate that RLHF is a two-step process (reward model + RL) and that DPO's main innovation is collapsing this into a single, more direct step.
Read the original → Wikipedia: Reinforcement learning from human feedback
- #llm
- #generative ai
- #alignment
- #deep learning
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