DFM Mimir v1: An Open HRM Delivering Frontier Performance at 1B Parameters Using Only Permissible Post-Training Data
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Computer Science > Computation and Language
arXiv:2608.13517 (cs)
[Submitted on 13 Aug 2026]
Title:DFM Mimir v1: An Open HRM Delivering Frontier Performance at 1B Parameters Using Only Permissible Post-Training Data
Authors:Peter Schneider-Kamp, Jacob Nielsen, Gianluca Barmina, Kenneth Enevoldsen, Lukas Galke Poech
View a PDF of the paper titled DFM Mimir v1: An Open HRM Delivering Frontier Performance at 1B Parameters Using Only Permissible Post-Training Data, by Peter Schneider-Kamp and 4 other authors
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Abstract:Current large language model development relies on massive, often non-permissible datasets, creating a high barrier for researchers committed to open-source and ethically sourced data. We introduce Mimir v1, a 1-billion-parameter language model based on the Hierarchical Reasoning Model (HRM) architecture, that is trained from scratch and delivers highly competitive performance for English and sets a new state of the art for Danish using only permissible post-training data. Trained on a mixture of 161 datasets, Mimir v1 outperforms the original HRM-Text 1B and competes with larger frontier models like Qwen 3.5 4B and Gemma 4 E2B, tested across 20 benchmarks for English, Math & Code and Danish. The model is available on the Hugging Face Hub: this https URL
| Comments: | Technical Report, 20 Pages, 1 Model, Hierarchical Reasoning Model |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2608.13517 [cs.CL] |
| (or arXiv:2608.13517v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.13517
arXiv-issued DOI via DataCite (pending registration)
|
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View a PDF of the paper titled DFM Mimir v1: An Open HRM Delivering Frontier Performance at 1B Parameters Using Only Permissible Post-Training Data, by Peter Schneider-Kamp and 4 other authors
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