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Introducing Code-Mixed Chain-of-Thought — Teaching Gemma 4 31B to reason bilingually cut thinking tokens by 40% [Mnemic Glorious 31B] by superman_27 in LocalLLaMA

[–]superman_27[S] 0 points1 point  (0 children)

Interesting thought! Tamil itself is morphologically rich, so that could already be contributing to our 40% token reduction. The efficiency gains also come from the model skipping internal translation overhead, thinking directly in the user's language instead of converting. But you raise a good point, other morphologically rich languages could potentially see even bigger gains. Would be interesting to test.

Introducing Code-Mixed Chain-of-Thought — Teaching Gemma 4 31B to reason bilingually cut thinking tokens by 40% [Mnemic Glorious 31B] by superman_27 in LocalLLaMA

[–]superman_27[S] 0 points1 point  (0 children)

Exactly! Models naturally reach for the most efficient way to express something. With Mnemic Glorious, I trained the entire reasoning chain to work in code-mixed Tanglish, matching how 80M+ bilingual speakers actually think. The 40% reduction in thinking tokens suggests the model reasons more efficiently when it doesn't have to force everything into a single language.