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Policy Optimization: PPO and GRPO
PPO 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.
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LLM & GenAI FundamentalsExplain PPO and GRPO for LLM alignment. Why did GRPO drop the value model?→LLM & GenAI FundamentalsWalk through GRPO's implementation: group sampling, advantage normalization, and the failure modes you watch for.→LLM & GenAI FundamentalsYour RLHF model games the reward model instead of being genuinely helpful. How do you stop reward hacking?→LLM & GenAI FundamentalsWhat is reward-model overoptimization, and how do you detect and bound it during RLHF?→LLM & GenAI FundamentalsWhat is RLVR (reinforcement learning with verifiable rewards), and why does it work for reasoning models?→LLM & GenAI FundamentalsWalk through RLHF, then explain DPO and why it has largely displaced PPO-based RLHF.→
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