NLMR: Neuron-Level Locate-then-Merge for Multilingual Reasoning in English-Centric LLMs

Large Language Models (LLMs) exhibit strong reasoning capabilities in high-resource languages but struggle to generalize such capabilities to low-resource ones, making effective cross-lingual adaptation essential for multilingual reasoning. Common adaptation approaches, such as Supervised Fine-Tuning (SFT) on task data and Continual Pretraining (CPT) on language-specific text, face a fundamental challenge: SFT strengthens reasoning ability but provides limited target-language adaptation, whereas CPT enhances language-specific adaptation but can catastrophically forget previously acquired reasoning abilities. To address this challenge, we propose **N**euron-**L**evel Locate-then-Merge for **M**ultilingual **R**easoning **(NLMR)**, the first neuron-level locate-then-merge framework for multilingual reasoning that integrates the complementary strengths of task-specific SFT and language-specific CPT models. Specifically, we identify neurons critical to reasoning versus linguistic adaptation and selectively merge them, thus mitigating catastrophic forgetting while enhancing reasoning transfer across languages. To evaluate whether NLMR can jointly improve low-resource language reasoning and preserve language adaptation capability, we conduct extensive experiments across 10 languages, 3 reasoning benchmarks, and 2 backbone models, where NLMR consistently outperforms both single-trained models and strong parameter-level merging baselines. Furthermore, neuron-level analysis reveals distinct yet complementary subspaces for reasoning and language adaptation. By selectively preserving and integrating both subspaces, NLMR improves multilingual reasoning performance while also providing an interpretable view of how language and reasoning capabilities are transferred and combined.