reinforcement learning
Applied AI interview questions tagged reinforcement learning, across every topic.
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Concepts behind "reinforcement learning"
The curriculum that explains the ideas these questions test.
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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
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Training Reasoning Models: RLVR, PRM vs ORMReasoning models like o1 and R1 are not just bigger instruct models: they are trained with reinforcement learning where the reward comes from checking whether the final answer is correct, which teaches the model to generate long internal chains of thought. This page covers RL with verifiable rewards (and GRPO specifically), the split between process reward models that score each step and outcome reward models that score only the answer, and how that choice shapes test-time search. Applied AI interviews probe it to see if you understand where the reasoning ability actually comes from.🧠 Foundations of LLMs & GenAI🔒 Premium