Weird Re-Tokenization, symmetries and compression: research agenda

Language models break text into chunks called tokens before processing. According to LessWrong, researchers discovered that models can surprisingly understand and even produce text with different tokenization schemes they've never encountered, suggesting they operate at deeper semantic levels than expected. This discovery raises important questions about model robustness and alignment safety. It's now prompting investigation into whether training could be improved to prepare models better for these unexpected tokenization variations.

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