BPE is how GPT and most LLMs turn text into tokens, and building a tiny trainable version from a corpus is a favorite deep-dive. The screen for whether you understand the merge loop, deterministic tie-breaking, and round-trip decode, not just tiktoken.encode.
Updated Aug 2026 · Grounded in real Forward Deployed Engineer interview loops and written to a senior-engineer editorial bar.
BPE is how GPT and most LLMs turn text into tokens, and building a tiny trainable version from a corpus is a favorite deep-dive. The screen for whether you understand the merge loop, deterministic tie-breaking, and round-trip decode, not just tiktoken.encode.
This question separates the field hard. The training loop is the core: repeatedly count adjacent pair frequencies across the corpus, merge the most frequent pair everywhere, record the merge, repeat. The tells are non-deterministic tie-breaking (two pairs tied in frequency must resolve the same way every run), applying merges out of learned order at encode time, and failing to round-trip decode. A candidate who handles an unknown character at encode time by falling back to the base character vocabulary shows they understand why BPE never has true OOV.
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