Scale.ai·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Scale.ai ML engineer interview that went deep on RL alignment methods. One meaty question covering PPO, DPO, and GRPO back to back, with follow-ups on when you'd actually pick each in practice. Felt more like a whiteboard seminar than a standard interview.

Questions Asked (1)

Compare PPO, DPO, and GRPO as methods for aligning LLMs: walk through the objective each one optimizes, what kind of reward or preference data it needs, whether it requires a separate reward model or value function, the compute and memory tradeoffs, and the practical strengths and limitations of each. Then explain when you'd choose one over the others.

Technical Trade-offsSystem DesignAlgorithms & Data Structures

Author's notes

This was basically the whole interview packed into one question.

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AI HintsAI Generated

Suggested Approach

Structure your answer by first defining each method's objective and data requirements, then compare them along axes like reward modeling, value functions, and compute/memory. Finally, discuss practical trade-offs and when to choose each based on constraints and goals.

Pro tip: Emphasize that DPO eliminates the reward model and value function, making it simpler and more stable, but PPO and GRPO can be more sample-efficient and flexible with online feedback. Mention that GRPO is particularly useful when you have a reliable reward model and want to avoid training a value function.

1. Define Objectives

Clearly state the optimization objective of each method: PPO maximizes expected reward with a KL penalty, DPO directly optimizes the policy to match preference data, and GRPO optimizes relative rewards within a group without a value function.

2. Data and Reward Requirements

Explain the type of data each needs: PPO requires a reward model and online interaction, DPO needs pairwise preference data, and GRPO requires a reward model but no value function and can work with grouped samples.

3. Compute and Memory Trade-offs

Compare the computational and memory costs: PPO is heavy due to reward model and value function training, DPO is lighter as it only trains the policy, and GRPO sits in between by avoiding the value function but still using a reward model.

4. Strengths and Limitations

Discuss practical strengths and limitations: PPO is flexible but complex and unstable, DPO is simple and stable but may underperform with limited data, and GRPO reduces variance and compute but relies on a good reward model.

5. Selection Criteria

Provide guidance on when to choose each: use PPO when you have a reliable reward model and need online learning, DPO when you have abundant preference data and want simplicity, and GRPO when you want a balance and have grouped samples.

Key Points to Mention

PPO optimizes expected reward with a KL divergence penalty to prevent deviation from the original policy.
DPO reparameterizes the reward function to directly optimize the policy on preference pairs, eliminating the need for a separate reward model.
GRPO (Group Relative Policy Optimization) computes advantages relative to a group of responses, removing the need for a value function.
PPO requires training a reward model and a value function, leading to higher memory and compute costs.
DPO is simpler and more stable but may be less effective when preference data is limited or noisy.
GRPO reduces variance and computational overhead compared to PPO, but still depends on a reward model.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.