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Large Language Models
·
Module 11
Model Alignment and Preference Optimization
1
AI Alignment and LLM Failure Modes
Explain the concept of AI alignment and what failure modes (e.g., helpfulness, harmlessness) it seeks to address in LLMs.
2
The Three Stages of RLHF
Outline the three high-level stages of the Reinforcement Learning from Human Feedback (RLHF) pipeline.
3
Training Reward Models with Pairwise Preferences
Explain how a reward model is trained from pairwise human preference data to learn a scalar reward signal.
4
PPO's Role in SFT Model Optimization and Regularization
Describe the role of Proximal Policy Optimization (PPO) in optimizing the SFT model against the reward model while regularizing against policy shift.
5
Direct Preference Optimization (DPO): A Reward-Model-Free RLHF Alternative
Explain Direct Preference Optimization (DPO) as an alternative to RLHF that directly optimizes the policy using preference data, bypassing an explicit reward model.
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Model Adaptation and Fine-Tuning
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Model Evaluation and Deployment