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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, 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.
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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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