# Maistros: A Greek Large Language Model Adapted Through Knowledge Distillation From Large Reasoning Models

arXiv: https://arxiv.org/abs/2605.01870 · HTML: https://mlsanity.com/paper/2605.01870/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 21 of its 56 references that are in the map.
- First posted on arXiv 3 May 2026.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)
- Knowledge distillation / [Knowledge distillation for LLMs](https://mlsanity.com/distill/knowledge-distillation-for-llms/)

## Builds on (most cited first, 21 of 21)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Gemma 3 Technical Report](https://mlsanity.com/paper/2503.19786/) (2025)
- [OpenAI GPT-5 System Card](https://mlsanity.com/paper/2601.03267/) (2025)
- [Gemini: A Family of Highly Capable Multimodal Models](https://mlsanity.com/paper/2312.11805/) (2023)
- [Large Language Models: A Survey](https://mlsanity.com/paper/2402.06196/) (2024)
- [8-bit Optimizers via Block-wise Quantization](https://mlsanity.com/paper/2110.02861/) (2021)
- [Ministral 3](https://mlsanity.com/paper/2601.08584/) (2026)
- [Translationese in Machine Translation Evaluation](https://mlsanity.com/paper/1906.09833/) (2019)
- [Meltemi: The first open Large Language Model for Greek](https://mlsanity.com/paper/2407.20743/) (2024)
- [The Roots of Performance Disparity in Multilingual Language Models: Intrinsic Modeling Difficulty or Design Choices?](https://mlsanity.com/paper/2601.07220/) (2026)
- [Global PIQA: Evaluating Commonsense Reasoning Across 100+ Languages and Cultures](https://mlsanity.com/paper/2510.24081/) (2025)
- [LLaMandement: Large Language Models for Summarization of French Legislative Proposals](https://mlsanity.com/paper/2401.16182/) (2024)
- [EuroLLM-22B: Technical Report](https://mlsanity.com/paper/2602.05879/) (2026)
- [NLP for The Greek Language: A Longer Survey](https://mlsanity.com/paper/2408.10962/) (2024)
- [GreekMMLU: A Native-Sourced Multitask Benchmark for Evaluating Language Models in Greek](https://mlsanity.com/paper/2602.05150/) (2026)
- [GRDD+: An Extended Greek Dialectal Dataset with Cross-Architecture Fine-tuning Evaluation](https://mlsanity.com/paper/2511.03772/) (2025)
- [Evaluating Monolingual and Multilingual Large Language Models for Greek Question Answering: The DemosQA Benchmark](https://mlsanity.com/paper/2602.16811/) (2026)
- [Building a Strong Instruction Language Model for a Less-Resourced Language](https://mlsanity.com/paper/2603.01691/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have substantially advanced the field of Natural Language Processing (NLP), achieving state-of-the-art performance across a wide range of tasks. These improvements have been attributed, in part, to their emerging reasoning capabilities, which are enabled by large-scale training and increased model capacity. However, existing LLMs can generate erroneous responses when addressing complex queries that fall outside their training distribution, due to limited internal knowledge or the need for multi-step reasoning. To address these limitations, recent work has introduced large reasoning models (LRMs), which incorporate explicit internal reasoning processes to improve response accuracy. Additionally, state-of-the-art LRMs often comprise hundreds of billions of parameters and require several seconds per inference, even on advanced multi-GPU systems. These characteristics limit their practicality for deployment in conventional computing environments. Meanwhile, NLP research on multilingual LLMs continues to prioritize high-resource languages. However, these models exhibit limited performance in under-resourced languages, primarily due to insufficient language- and culture-specific training data. In this paper, we focus on Modern Greek, for which only a limited number of question answering (QA) datasets have been proposed, most of which are intended for model evaluation. To address this research gap in Greek QA, we make the following contributions: (i) CulturaQA, a high-quality LRM-generated and human-curated dataset, for Greek LLM training and evaluation; (ii) a memory-efficient LLM evaluation framework adaptable to diverse languages and QA tasks; (iii) Maistros 8B, a state-of-the-art open-weights Greek LLM developed via knowledge distillation and fine-tuning on CulturaQA; and (iv) a comprehensive evaluation of nine LLMs across nine human-curated Greek QA datasets.