{"ID":2842437,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.10628","arxiv_id":"2511.10628","title":"Instella: Fully Open Language Models with Stellar Performance","abstract":"Large language models (LLMs) have demonstrated remarkable performance across a wide range of tasks, yet the majority of high-performing models remain closed-source or partially open, limiting transparency and reproducibility. In this work, we introduce Instella, a family of fully open three billion parameter language models trained entirely on openly available data and codebase. Powered by AMD Instinct MI300X GPUs, Instella is developed through large-scale pre-training, general-purpose instruction tuning, and alignment with human preferences. Despite using substantially fewer pre-training tokens than many contemporaries, Instella achieves state-of-the-art results among fully open models and is competitive with leading open-weight models of comparable size. We further release two specialized variants: Instella-Long, capable of handling context lengths up to 128K tokens, and Instella-Math, a reasoning-focused model enhanced through supervised fine-tuning and reinforcement learning on mathematical tasks. Together, these contributions establish Instella as a transparent, performant, and versatile alternative for the community, advancing the goal of open and reproducible language modeling research.","short_abstract":"Large language models (LLMs) have demonstrated remarkable performance across a wide range of tasks, yet the majority of high-performing models remain closed-source or partially open, limiting transparency and reproducibility. In this work, we introduce Instella, a family of fully open three billion parameter language m...","url_abs":"https://arxiv.org/abs/2511.10628","url_pdf":"https://arxiv.org/pdf/2511.10628v2","authors":"[\"Jiang Liu\",\"Jialian Wu\",\"Xiaodong Yu\",\"Yusheng Su\",\"Prakamya Mishra\",\"Gowtham Ramesh\",\"Sudhanshu Ranjan\",\"Chaitanya Manem\",\"Ximeng Sun\",\"Ze Wang\",\"Pratik Prabhanjan Brahma\",\"Zicheng Liu\",\"Emad Barsoum\"]","published":"2025-11-13T18:52:46Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\",\"cs.LG\"]","methods":"[\"Reinforcement Learning\",\"Large Language Model\",\"Language Model\"]","has_code":false}