GRPO Deep Dive: Critic-Free RL, Parallelism, MLA, and Reward Design for a Reasoning LLM
Company: Amazon
Role: Applied Scientist
Category: Machine Learning
Difficulty: hard
Interview Round: Technical Screen
Overview: This question assesses understanding of reinforcement learning algorithms used to post-train large reasoning language models, including critic-free policy optimization, distributed training parallelism, and reward design. It is commonly asked in machine learning engineering interviews to gauge depth of practical, systems-level knowledge rather than textbook familiarity with RL theory. The question probes conceptual grasp alongside applied reasoning about real training failure modes.
