Qwen 3.8 writes math instead of sentences, users say

A 125-upvote thread on r/LocalLLaMA collects examples of Qwen 3.8 explaining itself in set notation. One post shows a tool-availability rule written as an intersection of three sets instead of a sentence a person would say out loud.

The thread's poster has a theory: this is what happens when you train a model to minimize tokens per unit of intelligence. The model finds the densest possible encoding of an idea. A human then has to decompress it back into something readable. Nobody benchmarks that decompression cost, so it doesn't show up anywhere the model gets graded on.

I've hit versions of this problem building with local models. Compact output looks efficient in a benchmark and reads like a math proof in an actual chat window. The two things aren't the same axis, but training tends to optimize whichever one is measured.

Here's what I'd bet against: that this stays a Qwen quirk. Any model tuned hard for token efficiency without a separate readability check is going to produce the same set-notation instinct, because the training signal rewards compression regardless of who's on the other end of the output.

The thread doesn't say whether Alibaba has responded or whether a prompt-level fix (asking for plain language explicitly) reliably works around it. If you're picking a local model for anything user-facing, that's worth testing yourself before you trust the model to explain its own reasoning in words a person would use.