Comment by hnfong

I'd mentally put this in the same box as "chain of thought", where models perform better when explicitly describing the reasoning steps. The only difference in your case being that the model is undertrained in non-English data, so it's "next token prediction" of non-English prompts is less robust, and thus explicitly converting to English and then back makes it better.

This is probably the case for the "deep reasoning" models as well. If you for example try DeepSeek R1, it will likely reason in either English or Chinese (where it presumably is well trained) even if the prompt is in other languages.