Although the community has tackled the acquisition of high-quality Arabic pretraining data, we still lack large-scale, multi-turn Arabic datasets that include reasoning and tool calling. Naive translation can work at the pretraining scale, but post-training demands much higher quality, which requires a stricter approach to dataset curation. In this work, we introduce SmolKalam, a translation of Smoltalk2 that uses a multi-model ensemble translation pipeline, applies quality filtering, and examines effective translation techniques for traditional decoder-only models through ablations.
@article{arxiv.2511.18411,
title = {SmolKalam: Ensemble Quality-Filtered Translation at Scale for High Quality Arabic Post-Training Data},
author = {Sultan Alrashed and Chadi Helwe and Francesco Orabona},
journal= {arXiv preprint arXiv:2511.18411},
year = {2025}
}