scalable oversight
AI, ML & GenAI interview questions tagged scalable oversight, across every topic.
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Concepts behind "scalable oversight"
The curriculum that explains the ideas these questions test.
Core
Constitutional AI and RLAIFRLAIF (RL from AI Feedback) swaps human preference labels for AI-generated ones, pushing alignment past the human-labeling bottleneck. Constitutional AI is Anthropic's particular version: the model critiques and revises its own outputs against a written set of principles (a constitution), producing the preference data from those principles. The upside is scalability, consistency, and explicit, editable values; the downside is the AI judge's own biases. AI, ML, and GenAI interviews probe it because it is how alignment scales and how values become explicit and auditable.🧠 Foundations of LLMs & GenAISign in
Core
Alignment: Outer, Inner, and Scalable OversightAlignment is getting a system to pursue what we actually want rather than what we literally specified. Outer alignment asks whether the objective is right, and it fails as reward hacking, overoptimization, and sycophancy. Inner alignment asks whether the model internalized the goal or something merely correlated with it. Scalable oversight asks how humans supervise models they can no longer evaluate. AI, ML, and GenAI interviews probe this because RLHF, DPO, and Constitutional AI are mechanisms, and the frame beneath them is what tells you when they will fail.🧠 Foundations of LLMs & GenAISign in