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this post was submitted on 03 Aug 2026
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It's beneficial for reasoning to have models trained in a few languages. Chinese is a good one because one character is one word is one token.
Chinese being more token efficient is a myth, and seems to stem from the superficial fact that characters are only visually more space efficient.
The fact that each Chinese character takes up 3 bytes (as opposed to 1 byte of English), words in Chinese typically require compounds of several characters, and that tokenizers have a limited vocabulary limited to mostly English means that Chinese is actually token inefficient.
No, Chinese Is Not More Token-Efficient Than English for LLMs | markhuang.ai - https://markhuang.ai/blog/chinese-token-myth
I didn't say it's more token efficient. I said training multiple languages improves reasoning.
Yup, anthropic's flagship models regularly output CJK or Cyrillic characters in heavy workloads for me
When they start hallucinating they output Klingon to me, and to make matters worse, with grammatical errors.
Lol I've been running linguistic research and it's funny when they just combine two scripts together into one word