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        <title>AdaMame: A Training Recipe for Adaptive Multilingual Reasoning</title>
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    <abstract>While Large Reasoning Models (LRMs) show strong performance in English, they often fail to reason in the language of the query, a phenomenon known as language collapse. Existing RL-based fixes typically add a binary language fidelity reward to the accuracy objective, yet still incur trade-off in accuracy, mid-trace code-switching, and excessive token usage. In this work, we propose AdaMame, a two-stage training recipe for multilingual mathematical reasoning that addresses these limitations by adaptively aligning the reasoning language to the query language without compromising accuracy. The first SFT stage fine-tunes on non-MT reasoning traces across five languages to establish multilingual reasoning capability. In the subsequent RL stage, we introduce AdaMame-GRPO, an adaptation of Group Relative Policy Optimization (GRPO) in which a query-conditioned alignment factor grows progressively during training, guiding the model to first explore diverse reasoning languages before exploiting reasoning in the query language. Evaluated across two benchmarks, two LRMs, and 12 languages, AdaMame-GRPO achieves Pareto-optimal performance across reasoning accuracy, language fidelity, and token efficiency over all baselines, with the strongest gains on out-of-domain, lower-resource languages.</abstract>
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%0 Conference Proceedings
%T AdaMame: A Training Recipe for Adaptive Multilingual Reasoning
%A Ki, Dayeon
%A Duh, Kevin
%A Carpuat, Marine
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F ki-etal-2026-adamame
%X While Large Reasoning Models (LRMs) show strong performance in English, they often fail to reason in the language of the query, a phenomenon known as language collapse. Existing RL-based fixes typically add a binary language fidelity reward to the accuracy objective, yet still incur trade-off in accuracy, mid-trace code-switching, and excessive token usage. In this work, we propose AdaMame, a two-stage training recipe for multilingual mathematical reasoning that addresses these limitations by adaptively aligning the reasoning language to the query language without compromising accuracy. The first SFT stage fine-tunes on non-MT reasoning traces across five languages to establish multilingual reasoning capability. In the subsequent RL stage, we introduce AdaMame-GRPO, an adaptation of Group Relative Policy Optimization (GRPO) in which a query-conditioned alignment factor grows progressively during training, guiding the model to first explore diverse reasoning languages before exploiting reasoning in the query language. Evaluated across two benchmarks, two LRMs, and 12 languages, AdaMame-GRPO achieves Pareto-optimal performance across reasoning accuracy, language fidelity, and token efficiency over all baselines, with the strongest gains on out-of-domain, lower-resource languages.
%U https://aclanthology.org/2026.emnlp-main.796/
%P 18490-18511

Markdown (Informal)

[AdaMame: A Training Recipe for Adaptive Multilingual Reasoning](https://aclanthology.org/2026.emnlp-main.796/) (Ki et al., EMNLP 2026)

• AdaMame: A Training Recipe for Adaptive Multilingual Reasoning (Ki et al., EMNLP 2026)

ACL

• Dayeon Ki, Kevin Duh, and Marine Carpuat. 2026. AdaMame: A Training Recipe for Adaptive Multilingual Reasoning. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 18490–18511, Budapest, Hungary. Association for Computational Linguistics.