The development of high-quality multilingual models depends on well-curated multilingual datasets, which are typically obtained through filtering large web-crawled corpora. Such filtering approaches must be effective across a wide range of languages in order to improve model quality proportionally across the entire linguistic space. In recent years multiple Natural Language Processing (NLP) approaches for data processing and Large Language Model (LLM) training were developed. However, the most significant progress has been made for rich-resource languages such as English and Chinese, leaving multilingual methods largely underexplored. In this work, we present data selection strategies based on Internet registers (genres) and Web Document Score (WDS). We study 15 European languages utilizing HPLT 3.0 datasets. We train and evaluate four small generative language models per language, and compare how specific registers and WDS affect model training in separate and combined approaches. We use five common evaluation tasks covering models’ linguistic, knowledge-based, and reasoning skills. We analyze the impact of the studied strategies on each language, their average performance, and show how combining register-based selection with WDS improves evaluation performance. These results indicate that both registers and WDS can generalize across multiple languages, offering a new path towards multilingual data filtering.

Open web-scale pre-training corpora remain concentrated in English, limiting multilingual LLM development. We introduce MultiSynt/MT, an open synthetic parallel corpus with approximately 4.8 trillion target-language tokens across 36 European languages, produced by translating 100 billion high-quality Nemotron-CC tokens with Tower+ and OPUS-MT/HPLT-MT systems. For many medium- and lower-resource European languages, this is the largest openly available pre-training resource. On a broad multilingual benchmark suite, reference LLMs trained on MultiSynt/MT reach the final score of HPLT 2.0, a native-data baseline, using roughly 72% fewer pre-training tokens, and outperform it by approximately 15% relative at a matched 100B-token training budget. Our analyses also identify evaluation blind spots: standard multiple-choice benchmarks miss translation-quality differences that a fluency-sensitive LLM-as-judge evaluation cleanly recovers on the trained LLMs (with no fluency deficit in MultiSynt itself), and Norwegian idiomatic and culturally grounded tasks remain better served by native data. We release the corpus, including row-aligned translations from multiple systems, to support controlled research on multilingual pre-training data and evaluation.