What is Tokenization Drift and How to Fix It?
The story
A model can behave perfectly one moment and degrade the next—without any change to your data, pipeline, or logic. The root cause often lies in something far more subtle: how your input is tokenized. Before a model processes text, it converts it into token IDs, and even minor formatting differences—like spacing, line breaks, or punctuation—can [ ] The post What is Tokenization Drift and How to Fix
From the source
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The impact goes deeper than just token IDs. During instruction tuning, models learn not only tasks but also the structure in which those tasks are presented—specific separators, prefixes, and formatting patterns. When your prompt deviates from these learned patterns, you are no longer operating within the model’s familiar distribution. The result isn’t confusion—it’s a model doing its best on inputs it was never optimized to handle.
In this article, we ll break this down using the GPT-2 tokenizer to show how small formatting changes affect tokens, and build a simple metric to measure drift across prompts.
Who and what
Key names and topics in this story: Tokenization Drift.
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