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arXiv · 2507.15849

The Impact of Language Mixing on Bilingual LLM Reasoning

Yihao Li·Jiayi Xin·Miranda Muqing Miao·Qi Long·Lyle Ungar
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Abstract

Proficient multilingual speakers often intentionally switch languages in the middle of a conversation. Similarly, recent reasoning-focused bilingual large language models (LLMs) with strong capabilities in both languages exhibit language mixing-alternating languages within their chain of thought. Discouraging this behavior in DeepSeek-R1 was found to degrade accuracy, suggesting that language mixing may benefit reasoning. In this work, we study language switching in Chinese-English bilingual reasoning models. We identify reinforcement learning with verifiable rewards (RLVR) as the critical training stage that leads to language mixing. We show that language mixing can enhance reasoning: enforcing monolingual decoding reduces accuracy by 5.6 percentage points on MATH500. Additionally, a lightweight probe can be trained to predict whether a potential language switch would benefit or harm reasoning, and when used to guide decoding, increases accuracy by 2.92 percentage points. Our findings suggest that language mixing is not merely a byproduct of multilingual training, but is a strategic reasoning behavior.

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Yihao Li, Jiayi Xin, Miranda Muqing Miao, Qi Long, Lyle Ungar. 2025-09-30. The Impact of Language Mixing on Bilingual LLM Reasoning. https://arxiv.org/abs/2507.15849

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