LLM Fundamentals: Tokenization Design and KL-Regularized SFT
Company: Amazon
Role: Applied Scientist
Category: Machine Learning
Difficulty: medium
Interview Round: Onsite
Overview: This question evaluates depth of knowledge in large language model fundamentals, specifically subword tokenization design and KL-regularized supervised fine-tuning objectives. It tests conceptual understanding of why these techniques are used in modern LLM training pipelines, commonly asked in machine learning engineering interviews to assess architectural reasoning beyond surface-level API familiarity.
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