By Lukas Edman, Alexander FraserSource:
[arXiv cs.CL](https://arxiv.org/list/cs.CL/recent)
arXiv:2610.19294v1 Announce Type: new
Abstract: This work concerns our submission for the Multilingual track of the 2026 BabyLM Challenge. We improve on our baseline using data that has been summarized and translated by an
LLM, showing that higher-quality data can come from synthetic sources. We further improve with 2 simple changes: using a larger model, and using a character-level vocabulary. Both of these changes requireLoRA-stylefine-tuningfor realizing their full potential. We dub our final Qwen3-based model Qwenfant, which, at the time of submission, ranks 1st in the Multilingual track. Get AI news in your inbox
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source & further reading
machinebrief.com — original article
TopicMerge: A Divide and Conquer Approach to Topic Modeling
Copy-Paste Language Models
Titan-Llama: Retrofitting Pre-trained LLMs with External Neural Memory Modules