What happens when reasoning tests assume the model already knows the language?
The core problem is that many reasoning tests in low-resource languages inadvertently measure memorization rather than genuine reasoning. The LingOly benchmark was designed to avoid this by using obscure and extinct languages with no prior training data, and it found that models perform poorly on harder problems—the top model only reached 38.7% accuracy, a 24.7% improvement over a no-context baseline [1]. That means even the best model was barely better than random guessing when it couldn't fall back on memorized patterns, highlighting how fragile reasoning becomes when the assumption of prior knowledge is removed.
The benchmark also showed a clear resource gradient: the higher the resource of the language, the better the scores [1]. This suggests that models rely on statistical regularities from high-resource languages, and when those are absent, reasoning collapses. So, when you strip away the crutch of familiar linguistic patterns, current LLMs struggle to perform the multi-step inference that human puzzle-solvers can do.
Can reasoning be transferred from high-resource languages to low-resource ones?
Yes, but only with deliberate training that separates language understanding from reasoning. A 2025 study introduced English-Pivoted Chain-of-Thought (CoT) training, where the model is fine-tuned to generate reasoning steps in English and then output the final answer in the target low-resource language [2]. This approach improved mathematical reasoning by up to 28.33% over other baselines in extremely low-resource scenarios, showing that leveraging the model's internal alignment toward English can unlock reasoning abilities that would otherwise remain dormant.
The same study found that explicitly separating language understanding from reasoning—through mixed-language CoT or two-stage training—enhances crosslingual reasoning [2]. This suggests that the bottleneck isn't the model's reasoning capacity per se, but its ability to map low-resource input to its internal reasoning space. However, this transfer is not automatic; it requires supervised fine-tuning with data in the target language, which may not always be available.
Does having more data in the low-resource language itself help?
Yes, but it's not just about volume—it's about having a complete data ecosystem that covers all stages of model development. The Tibetan Foundation Dataset (TFD) is the first structured dataset for Tibetan that includes pre-training, instruction tuning, safety alignment, preference optimization, and reasoning supervision, totaling over 11 billion tokens [5]. Training the Sun-Shine family of Tibetan LLMs on this dataset led to substantial improvements over strong baselines on understanding, safety, reasoning, and generation benchmarks [5]. This shows that when you provide a low-resource language with the same kind of comprehensive data that high-resource languages enjoy, reasoning performance can be significantly boosted.
This finding complements the English-pivoted approach: while bridging through English helps, having native-language data for reasoning (like the TIBSTC-CoT chain-of-thought dataset) is also crucial. The two strategies are not mutually exclusive; they address different parts of the problem. The English-pivoted method helps when native data is scarce, while a full data ecosystem is the ideal long-term solution.
How do we know if reasoning is actually working in low-resource languages?
Evaluation itself is a challenge. Standard reference-based metrics often fail for low-resource languages, so researchers are turning to LLM-as-a-Judge methods. A 2026 study on Romanian found that evaluation strategies must be metric-specific: granular decomposition achieved 96% human alignment for faithfulness, while comparative ranking worked best for answer relevance at 90% [4]. This suggests that there's no one-size-fits-all way to measure reasoning quality in low-resource languages, and that even automated judges need careful adaptation.
Additionally, reasoning-based safeguards can be more robust than classifier-based ones. ConsistentGuard, a reasoning-based multilingual safeguard, outperformed larger models trained with significantly more data, using only 1,000 training samples across six languages [3]. This indicates that reasoning can also be used to improve safety and alignment, not just to solve puzzles or math problems. However, the same study noted that lightweight models struggle with complex reasoning in low-resource languages, so the benefits are not universal across model sizes.
About These Sources
This answer is built on 5 peer-reviewed studies — published from 2024 to 2026, 5 from 2024 or later — selected as the most relevant from 5 studies that passed quality screening, drawn from 49 papers retrieved from a database of over 500 million.
Sources used in this answer
LINGOLY: A Benchmark of Olympiad-Level Linguistic Reasoning Puzzles in Low-Resource and Extinct Languages
The LingOly benchmark, covering 90+ low-resource and extinct languages, found that even the top LLM scored only 38.7% accuracy on hard linguistic puzzles, just 24.7% above a no-context baseline, indicating that true multi-step out-of-domain reasoning remains a challenge.
Reasoning Transfer for an Extremely Low-Resource and Endangered Language: Bridging Languages Through Sample-Efficient Language Understanding
English-Pivoted CoT Training, which fine-tunes LLMs to generate reasoning in English and output in the target low-resource language, improved mathematical reasoning by up to 28.33% over baselines in extremely low-resource scenarios, and the study released LC2024, the first mathematical benchmark in Irish.
Unlocking LLM Safeguards for Low-Resource Languages via Reasoning and Alignment with Minimal Training Data
ConsistentGuard, a reasoning-based multilingual safeguard, outperformed larger models trained with more data using only 1,000 training samples across six languages, demonstrating that reasoning can enhance safety and alignment in low-resource languages.
LLM-as-a-Judge for Low-Resource Languages: Adapting Ragas and Comparative Ranking for Romanian
In evaluating RAG systems for Romanian, granular decomposition achieved 96% human alignment for faithfulness with Gemini 2.5 Pro, while comparative ranking reached 90% for answer relevance, showing that evaluation strategies must be metric-specific and that lightweight models struggle with complex reasoning in low-resource languages.
TFD: A Comprehensive Structured Tibetan Foundation Dataset for Low-Resource Language Processing and Large-Scale Modeling
The Tibetan Foundation Dataset (TFD), with over 11 billion tokens covering all LLM development stages including reasoning supervision, enabled the Sun-Shine Tibetan LLMs to achieve substantial improvements over strong baselines on understanding, safety, reasoning, and generation benchmarks.