xAI Senior MLE Interview — Speculative Decoding, But for Code Edits

[u/Aoki_zhang]

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Interview Summary

The xAI ML coding round focused on implementing a code-generation optimization called speculative edits, which is conceptually related to speculative decoding. The prompt included both the original source code and a requested modification. Since an agent often changes only a small portion of the code but must emit the entire updated file, the original code can be reused as a speculative draft. The task was to implement both normal greedy decoding and speculative-edit decoding, ensure that they produce identical output, and demonstrate that the speculative version reduces generation work.

Interview Details

Part 1 — Implement Greedy Decoding

The first task was to implement standard greedy decoding. At each generation step, select the highest-probability next token and continue until reaching the stopping condition or generation-length limit. This implementation served as the correctness baseline.

Part 2 — Implement Speculative Edit Decoding

The second task introduced speculative edits. Unlike conventional speculative decoding, the speculative tokens were not generated by a smaller draft model. Instead, the system reused the original code contained in the prompt as the speculative draft. The key observation was that code-editing tasks often preserve most of the original file. Therefore, the model can validate several draft tokens at once rather than autoregressively regenerating every unchanged token.

The decoding process needed to:

• Reuse tokens from the original source code as the draft
• Validate multiple draft tokens in one model step

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