Explain LLM training, RL, and evaluation
Quick Overview
This question evaluates understanding of the full large language model lifecycle and associated competencies, including pre-training, supervised fine-tuning, preference optimization, reinforcement learning–based post-training, reward design, optimization stability, common failure modes, and evaluation metrics.
Cohere
Dec 25, 2025, 12:00 AM
Machine Learning Engineer
Onsite
Machine Learning
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Explain how you would build and improve a modern large language model across the full lifecycle: pre-training, post-training, optimization, and evaluation. Compare the roles of pre-training, supervised fine-tuning, preference optimization, and reinforcement learning. In particular, discuss RL-based post-training for instruction following or reasoning, including reward design, optimization stability, and common failure modes.
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