ppo
Applied AI 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 becomes a helpful, harmless assistant. It has 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 are hard to specify as a loss. Applied-AI 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, updating the policy in small, clipped steps to stay stable; GRPO (used by DeepSeek-R1) drops PPO's separate value network and instead normalizes rewards within a group of samples, which is simpler and cheaper for LLMs. Applied-AI 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