← Anthropic Interview Insights
The bugs were all in the core training logic.
Start by explaining GRPO's core algorithm and typical implementation structure, then systematically trace the code to identify deviations from the expected behavior. For each bug, describe its symptom, root cause, and fix, prioritizing correctness and clarity.
Pro tip: Demonstrate a deep understanding of GRPO's mathematical foundations and common pitfalls, such as incorrect advantage normalization or policy ratio clipping, to show you can debug beyond surface-level issues.
Briefly outline GRPO's objective, including group-relative advantage estimation and policy optimization, and recall the standard pseudocode to establish a baseline for comparison.
Quickly review the code for syntax errors, undefined variables, or incorrect function calls that could cause immediate failures.
Follow the flow of inputs through the model, reward computation, advantage calculation, and loss to identify logical inconsistencies or mathematical errors.
For each bug found, articulate the incorrect behavior, explain why it's wrong, and propose a corrected version, ensuring the fix aligns with GRPO's principles.
Mentally simulate the corrected code with a simple example to verify correctness, and mention any potential trade-offs or edge cases affected by the fixes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.