M-Thinker: Advancing Multilingual Reasoning for LRMs via Dual-Reward Reinforcement Learning

M-Thinker: Advancing Multilingual Reasoning for LRMs via Dual-Reward Reinforcement Learning | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 2 December 2025 V1 Latest version Share on M-Thinker: Advancing Multilingual Reasoning for LRMs via Dual-Reward Reinforcement Learning Author : Arai Kaoru 0009-0003-4901-3726 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176463758.86744830/v1 130 views 140 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Large Reasoning Models (LRMs) have achieved remarkable performance on complex reasoning tasks by adopting the "think-thenanswer" paradigm, which enhances both accuracy and interpretability. However, current LRMs exhibit two critical limitations when processing non-English languages: (1) They often struggle to maintain input-output language consistency; (2) They generally perform poorly with wrong reasoning paths and lower answer accuracy compared to English. These limitations significantly degrade the user experience for non-English speakers and hinder the global deployment of LRMs. To address these limitations, we propose M-Thinker, which is trained by the GRPO algorithm that involves a Language Consistency (LC) reward and a novel Cross-lingual Thinking Alignment (CTA) reward. Specifically, the LC reward defines a strict constraint on the language consistency between the input, thought, and answer. Besides, the CTA reward compares the model's non-English reasoning paths with its English reasoning path to transfer its own reasoning capability from English to non-English languages. Through an iterative RL procedure, our M-Thinker-1.5B/7B models not only achieve nearly 100% language consistency and superior performance on two multilingual benchmarks (MMATH and PolyMath), but also exhibit excellent generalization on out-of-domain languages. Supplementary Material File (manuscript1.pdf) Download 1.20 MB Information & Authors Information Version history V1 Version 1 02 December 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords cross-lingual thinking alignment language consistency large reasoning models multilingual reasoning reinforcement learning reward modeling Authors Affiliations Arai Kaoru 0009-0003-4901-3726 [email protected] Mongolian National University of Education View all articles by this author Metrics & Citations Metrics Article Usage 130 views 140 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Arai Kaoru. M-Thinker: Advancing Multilingual Reasoning for LRMs via Dual-Reward Reinforcement Learning. Authorea . 02 December 2025. DOI: https://doi.org/10.22541/au.176463758.86744830/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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