The author outlines various techniques for training AI systems to be aligned and behave ethically. These methods involve leveraging internal model states or external outputs as reward signals, adjusting training data distributions, and employing different learning objectives such as supervised fine-tuning, reinforcement learning, or training on declarative facts. The approach also considers using ensemble methods with rejection sampling and factored cognition to create a more robustly aligned system, emphasizing the importance of interrogating models to detect potential sabotage. AI
IMPACT Provides a structured overview of AI alignment strategies, useful for researchers and developers.
RANK_REASON The item details existing research techniques for AI alignment. [lever_c_demoted from research: ic=1 ai=1.0]
- deliberative alignment
- factored cognition
- process supervision
- reinforcement learning
- rejection sampling
- supervised fine-tuning
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