Openai·Software Engineer·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

OpenAI SWE interview where they handed me a broken PyTorch GRPO training loop and told me to find what was wrong and fix it. Definitely the most domain-specific coding round I've sat through, you really need to know RL fine-tuning internals cold.

Questions Asked (1)

Given a partially implemented PyTorch GRPO training loop for a language model, identify the bugs and correctness issues, then fix them. The implementation covers group sampling, advantage normalization, KL penalty against a reference policy, and the policy gradient update.

Technical Trade-offsAlgorithms & Data StructuresSystem Design

Author's notes

This was a live debugging exercise, not a design question.

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AI HintsAI Generated

Suggested Approach

First, walk through the code to understand the intended GRPO algorithm and identify deviations from the paper or standard implementations. Then, systematically check each component (sampling, advantage normalization, KL penalty, policy update) for common bugs such as incorrect normalization, missing detach, or sign errors. Finally, propose fixes and explain why they are correct, emphasizing numerical stability and correctness.

Pro tip: Mention that GRPO is similar to PPO but without a value function, so the advantage is computed as the normalized reward within each group; this shows you understand the algorithm's core idea and can spot deviations quickly.

1. Understand the GRPO algorithm

Briefly explain the GRPO objective: for each prompt, sample a group of responses, compute rewards, normalize advantages within the group, and update the policy with a KL penalty against a reference model.

2. Review group sampling and reward computation

Check that multiple responses are sampled per prompt, rewards are computed correctly, and the group structure is maintained for advantage normalization.

3. Inspect advantage normalization

Verify that advantages are normalized per group (e.g., subtract mean, divide by std) and that normalization is done correctly without leaking information across groups.

4. Examine KL penalty and policy update

Ensure the KL penalty is computed against a reference policy (often detached), added with the correct sign and coefficient, and that the policy gradient update uses the correct loss (e.g., -advantage * log_prob) with proper masking.

5. Propose fixes and validate

For each identified bug, suggest a concrete fix and explain how it corrects the issue, then mentally run through a small example to validate the corrected code.

Key Points to Mention

Advantage normalization should be done per group (e.g., subtract mean and divide by std within each group of responses for the same prompt).
KL penalty must be computed against a reference policy that is not updated, and typically the reference log probabilities should be detached to avoid backpropagating through them.
The policy gradient loss should be negative of advantage times log probability, and often a ratio term (like in PPO) is used, but in GRPO it's simplified.
Masking is crucial to ignore padding tokens and ensure loss is only computed on generated tokens.
Numerical stability: use small epsilon in normalization and clamp ratios or log probabilities to avoid NaNs.
Detach advantages and rewards from the computation graph to prevent gradients flowing into them.

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