arXiv:2607.00485v1 Announce Type: new Abstract: Large reasoning models (LRMs) have achieved strong reasoning capabilities in English, yet their performance degrades significantly when required to reason in other languages. A natural solution is to transfer the model's English reasoning ability to target languages. However, existing transfer approaches typically rely on distilled target-language reasoning traces from stronger LRMs or online supervision from external judge models, which are costly and difficult to scale. In this paper, we propose PCS (Progressive Code-Switching), a more efficien
The proliferation of large reasoning models (LRMs) has highlighted a critical performance gap in non-English languages, creating an urgent need for more efficient multilingual transfer methods that avoid costly existing approaches.
Improving the multilingual capabilities of LRMs efficiently will accelerate their global adoption and reduce dependency on expensive, data-intensive localization processes, democratizing advanced AI reasoning across diverse linguistic contexts.
The proposed Progressive Code-Switching (PCS) method suggests a more scalable and cost-effective pathway to true multilingual AI, potentially shifting how AI models are trained and deployed for global markets.
- · AI developers
- · Non-English speaking markets
- · Multilingual content platforms
- · Emerging market tech sectors
- · Companies relying on expensive AI localization
- · Monolingual AI services
- · Early-stage reasoning model startups
More accurate and accessible AI reasoning models in non-English languages become available.
This efficiency reduces the cost of deploying advanced AI globally, accelerating adoption in diverse linguistic regions.
Increased global AI accessibility fosters new industries and applications in localized contexts, potentially leading to new forms of digital inequality or empowerment based on regional AI infrastructure.
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