E fficient XL ang: Towards Improving Token Efficiency Through Cross-Lingual Reasoning (2025)

Abstract: Despite recent advances in Reasoning Language Models (RLMs), most research focuses solely on English, even though many models are pretrained on multilingual data. In this work, we investigate: Is English the most token-efficient language for reasoning? We evaluate three open-source RLMs: DeepSeek R1, Qwen 2.5, and Qwen 3, across four math datasets and seven typologically diverse languages. We find that reasoning in non-English languages not only reduces token usage, but also preserves accuracy. These gains persist even after translating the reasoning traces into English, suggesting genuine shifts in reasoning behavior rather than surface-level linguistic effects. The extent of improvement, however, depends on the model’s multilingual strength. Our findings motivate a broader view of reasoning in language models, highlighting the potential of multilingual reasoning and the importance of strong multilingual foundations. The code for our work can be found: https://github.com/microsoft/EfficientXLang.

## introduction recent advancements have shown that increasing inference-time compute can significantly enhance the downstream performance of large language models. in particular, rlms benefit from extended test-time computation through long chainsof-thought, resulting in improved reasoning capabilities and task performance @xcite . despite this progress, the majority of prior work has focused on reasoning in english, implicitly assuming it as the default or optimal language for thought @xcite . a key limitation of current rlms lies in the number of "thinking tokens" they consume during inference. since test-time compute scales with the number of tokens generated, longer reasoning traces di- * equal contribution. ## experiments to assess whether reasoning in other languages can enhance thinking token efficiency, we experiment with seven typologically diverse languages: chinese, russian, spanish, hindi, arabic, korean, and turkish, along with english. we prompt the reasoning models in english, instructing them to carry out their reasoning in the target language while providing the final answer in english. this setup allows us to isolate the effect of language on the reasoning process itself. for inferencing, we used the sglang framework @xcite , which facilitates the extraction of intermediate reasoning traces. 1 we now detail our experimental setup. datasets: we evaluate our hypothesis in a variety of math datasets, which we categorize into easy and difficult subsets based on the complexity of the problem and the typical performance of existing models. the difficult datasets include aime2024 (veeraboina, 2023) and aime2025 (art of problem solving, 2025a,b), which feature competition-level problems that require advanced reasoning. the easy datasets include amc23 (art of problem solving, 2023a,b), and math500 @xcite comprising of simpler problems. ## analysis this section explores a series of research questions aimed at validating our thesis. rq1: is english the optimal language for reasoning for lrms ? to analyze task and language effects in more detail, we examine deepseek r1 (due to its strong tlc) across datasets (tables 2 , 4 , fig. 1 , 2 ). detailed results for qwen 2.5 and qwen 3 are provided in appendix a.3. we observe that -for easy tasks (e.g., math500), language choice has little effect on accuracy, while non-english reasoning uses fewer tokens. in conclusion, multilingual reasoning traces are meaningfully different and often more concise. their brevity persists after translation, suggesting more efficient reasoning strategies-not just linguistic differences. ## related work efficient reasoning: recent interest in lrms has spurred investigations into reasoning efficiency as to why some tasks demand more reasoning tokens and whether models can answer correctly without explicit reasoning steps @xcite @xcite @xcite . this work explores reasoning efficiency from a multilingual perspective, analyzing whether certain reasoning paths across languages are both accurate and token-efficient. multilingual reasoning: prior studies showed that prompting models in the target language often underperforms compared to reasoning in english @xcite @xcite . however, rlms like deepseek r1 and qwen have shown stronger multilingual reasoning without explicit tuning @xcite @xcite . we build on this by introducing tighter evaluation bounds and fine-grained trace-level analysis, uncovering language-specific reasoning paths that are both accurate and tokenefficient. concurrently, several recent works have also begun to explore multilingual reasoning in depth @xcite . ## conclusion and future work in this work, we explore two central questions: (1) is english the optimal language for reasoning? and importantly, the effectiveness of this approach is tied to the model's underlying multilingual strength. our findings highlight the need to improve base multilingual alignment to better support efficient and high-quality reasoning across languages. future work should explore training and preference optimization across more diverse languages to unlock these benefits more broadly.