Explain LLM training, RL, and evaluation
Company: Cohere
Role: Machine Learning Engineer
Category: Machine Learning
Difficulty: medium
Interview Round: Onsite
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.
Quick Answer: 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.