TUDUM: A Turkish-Thinking Reasoning Pipeline for Qwen3.5-27B

What Happened

Researchers have released TUDUM, a pipeline designed to adapt Qwen3.5-27B—a 27-billion parameter thinking model from the Qwen family—for Turkish-language reasoning. The core innovation is not merely translating Turkish prompts and generating Turkish responses, but fundamentally retraining the model to think in Turkish. This involves restructuring the model’s internal reasoning chain to operate in Turkish, rather than relying on English as an intermediary language. The pipeline addresses tokenization inefficiencies, cultural context gaps, and syntactic differences that plague direct translation approaches.

Why It Matters

This work highlights a persistent blind spot in large language model development: most frontier reasoning models are optimized for English or a handful of high-resource languages. Turkish, with its agglutinative morphology and non-Indo-European syntax, poses unique challenges. Standard tokenizers often fragment Turkish words into excessive subword units, inflating inference costs and degrading reasoning coherence. TUDUM’s approach—retraining the reasoning pipeline itself rather than just fine-tuning on Turkish data—represents a shift from surface-level localization to deep linguistic adaptation.

The project also underscores a broader trend: as open-weight models like Qwen3.5 become more capable, the bottleneck shifts from raw performance to cultural and linguistic alignment. A model that can reason in English but only translate to Turkish will produce answers that feel foreign, miss local idioms, and fail at tasks requiring native-level comprehension (e.g., legal document analysis, poetry generation, or regional dialect handling). TUDUM’s pipeline methodology could serve as a template for adapting other thinking models to similarly underserved languages—from Basque to Swahili to Bengali.

Implications for AI Practitioners

For developers working with multilingual AI, TUDUM offers several practical lessons:

• Tokenization is not a solved problem. Even with modern tokenizers, low-resource languages suffer from inflated token counts. Practitioners should audit tokenizer efficiency for their target language before fine-tuning, as this directly impacts latency and cost.

• Reasoning chains must be language-native. Simply translating the final output of an English-thinking model introduces errors that compound during multi-step reasoning. TUDUM’s pipeline retrains the chain-of-thought process itself, which is more resource-intensive but yields higher fidelity.

• Open-weight models enable niche specialization. Qwen3.5-27B’s permissive license allows researchers to modify internal reasoning pathways—a capability locked behind APIs in proprietary models. This democratizes linguistic adaptation for languages that big labs deprioritize.

• Evaluation metrics need updating. Standard benchmarks like MMLU or GSM8K lack Turkish versions. TUDUM’s success will depend on creating culturally relevant evaluation sets that test genuine reasoning, not just translation accuracy.

Key Takeaways

• TUDUM adapts Qwen3.5-27B to reason natively in Turkish, not just translate English reasoning chains.
• The pipeline addresses tokenization inefficiencies and syntactic mismatches that degrade performance in agglutinative languages.
• This approach provides a reusable template for adapting thinking models to other underserved languages.
• Practitioners should prioritize language-native reasoning over output translation, and audit tokenizer efficiency as a first step.