ppo
AI, ML & GenAI interview questions tagged ppo, across every topic.
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Concepts behind "ppo"
The curriculum that explains the ideas these questions test.
Core
RLHF: Reinforcement Learning from Human FeedbackRLHF is how a raw next-token predictor turns into a helpful, harmless assistant. It runs in three stages: supervised fine-tuning on demonstrations, training a reward model on human preference comparisons, then optimizing the model against that reward (with a KL penalty to stay close to the base). It aligns the model to human preferences that resist specification as a loss. AI, ML, and GenAI engineer interviews probe it because it explains why instruct models behave well, where alignment data comes from, and the failure modes (reward hacking, sycophancy).🧠 Foundations of LLMs & GenAISign in
Advanced
Policy Optimization: PPO and GRPOPPO and GRPO are the reinforcement-learning algorithms that optimize an LLM against a reward, the RL step in RLHF and in training reasoning models. PPO is the established workhorse, nudging the policy in small, clipped steps to stay stable; GRPO (used by DeepSeek-R1) removes PPO's separate value network and instead normalizes rewards within a group of samples, which is simpler and cheaper for LLMs. AI, ML, and GenAI interviews probe it because it explains how alignment and reasoning training actually run, and why RL on verifiable rewards scales.🧠 Foundations of LLMs & GenAI🔒 Premium