arXiv:2606.15080v1 Announce Type: new 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 quer
The proliferation of Large Language Models (LLMs) globally necessitates effective multilingual reasoning capabilities to move beyond English-centric development.
This development addresses a significant limitation in current AI models, potentially unlocking broader global adoption and utility for advanced AI reasoning applications.
AI models can now more effectively reason in multiple languages without significant trade-offs in accuracy or efficiency, reducing 'language collapse'.
- · Multilingual AI users
- · AI developers focused on global markets
- · Companies operating in non-English speaking regions
- · Monolingual AI research paradigms
- · Models reliant solely on English data for reasoning tasks
Improved performance of AI models in diverse linguistic contexts.
Accelerated development and adoption of AI technologies in non-English speaking markets, potentially leading to new economic opportunities.
Enhanced global access to advanced AI capabilities, potentially democratizing AI but also raising new geopolitical considerations around AI dominance.
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Read at arXiv cs.CL