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The development of language models capable of reasoning in multiple languages represents one of the most complex challenges in contemporary artificial intelligence. Traditionally, multilingual systems translate input into a dominant language —usually English—, process internally, and return the response in the original language. However, this approach introduces cultural biases and semantic losses. The TUDUM project, applied to the Qwen3.5-27B model, directly addresses this limitation by training the model so that its chain of thought —the internal 'scratchpad'— is expressed directly in Turkish, not just the final response. This is achieved through a combination of supervised fine-tuning with Turkish reasoning examples and reinforcement learning based on GRPO. The published results are mixed: while response length is reduced and idiomatic consistency increases, performance on mathematical benchmarks drops slightly compared to the base model. This case illustrates the tension between linguistic fidelity and technical precision.
For companies seeking to implement artificial intelligence with multilingual capabilities, this type of research offers valuable lessons. It is not enough to translate interfaces; the underlying reasoning must align with the culture and cognitive structures of users. At Q2BSTUDIO, we understand these nuances and offer artificial intelligence solutions for businesses that go beyond simple language processing. Our services range from custom applications integrating language models adapted to specific domains, to AI agents capable of reasoning contextually in multiple languages. Additionally, we combine these capabilities with cloud platforms such as AWS and Azure cloud services to ensure scalability and security.
Cybersecurity also plays a critical role when deploying reasoning models. A system that exposes its chain of thought can reveal sensitive information if not properly protected. Therefore, at Q2BSTUDIO, we incorporate cybersecurity practices in all phases of custom software development. Likewise, business intelligence is enhanced when language models are connected to corporate data: with tools like Power BI, it is possible to visualize insights extracted from dialogues with intelligent agents. Our team combines business intelligence services with AI to offer dashboards that reflect the real-time performance of conversational assistants.
Ultimately, the TUDUM pipeline demonstrates that adapting the internal reasoning of a language model to a language is technically viable, although it requires a careful balance between precision and authenticity. For organizations, the key lies in having a technology partner that understands these complexities. At Q2BSTUDIO, we offer comprehensive support from conceptualization to implementation, whether developing custom software or creating personalized AI agents. If your company seeks to take artificial intelligence to the next level, we invite you to explore our solutions.
