ReasonXL: A Large-Scale Corpus for Language-Specific Reasoning in LLMs

Abstract:Despite advances in multilingual capabilities, most large language models (LLMs) remain English-centric in their training and, crucially, in the reasoning traces they produce: even for non-English problems, they predominantly reason in English, leaving non-English users with intermediate reasoning they cannot follow. We address this disparity with two contributions. **(a)** We introduce $\texttt{ReasonXL}$, the first large-scale parallel corpus of cross-domain reasoning traces in five European languages (English, German, French, Italian, and Spanish): over 2.5M aligned samples (11B tokens) per language, each comprising prompt, reasoning trace, and final output, enabling direct supervision of language-specific reasoning. **(b)** Using $\texttt{ReasonXL}$, we demonstrate that LLMs can be adapted to reason entirely in a target language through a simple two-stage pipeline of supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR), matching or exceeding the base models' English-reasoning performance in two of four languages and recovering most of it in the remaining two.

Computation and Language (cs.CL)

arXiv:2604.12378 [cs.CL]

(or arXiv:2604.12378v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2604.12378 Focus to learn more arXiv-issued DOI via DataCite

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