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Explain LLM training, RL, and evaluation

CohereMachine Learning Engineer · Onsite
MediumMachine Learning EngineerOnsiteMachine Learning

Last updated

This question tests your conceptual understanding of the end-to-end LLM lifecycle, specifically the transition from raw text pre-training to alignment and evaluation.

The LLM Lifecycle Architecture

As an ML Engineer at an AI research lab, you are tasked with outlining the development pipeline for a new foundation model. Explain the technical progression from pre-training to a deployed, helpful assistant.

Task

  1. Pre-training: Describe the objective function typically used and the significance of data quality versus quantity.
  2. Post-training Alignment: Contrast Supervised Fine-Tuning (SFT) with Reinforcement Learning from Human Feedback (RLHF). Why is RLHF often necessary even if an SFT model shows high accuracy?
  3. Reward Modeling: If you were implementing PPO (Proximal Policy Optimization) or DPO (Direct Preference Optimization), what are the primary challenges in training the reward model or preference objective?
  4. Evaluation: How would you design a robust evaluation framework that accounts for both factual grounding and safety, and how do you detect if your model is experiencing reward hacking?

Hints

Hint 1

Consider the difference between predicting the next token and optimizing for human preference metrics.

Hint 2

Think about the 'alignment tax'—why does improving helpfulness sometimes reduce factual accuracy?

Hint 3

Reflect on how reward models can overfit to specific annotator biases or common patterns in training data.

Follow-up questions

  • How does the choice of PPO versus DPO change the computational requirements of your training cluster?
  • What specific metrics would you look at to detect catastrophic forgetting during the fine-tuning phase?
  • In a production environment, how would you implement a 'refusal mechanism' for harmful queries without degrading the model's performance on benign requests?