HRM-Text: Efficient Pretraining Beyond Scaling
arXiv cs.CL·Guan Wang, Changling Liu, Chenyu Wang, Cai Zhou, Yuhao Sun, Yifei Wu, Shuai Zhen, Luca Scimeca, Yasin Abbasi Yadkori
8h agoQuick Take
HRM-Text introduces a sample-efficient pretraining approach for language models using hierarchical recurrent structures.
Key Points
- Replaces Transformers with Hierarchical Recurrent Model.
- Achieves competitive performance with significantly fewer tokens.
- Makes pretraining accessible to broader research community.
📖 Reader Mode
~2 min readAbstract:The current pretraining paradigm for large language models relies on massive compute and internet-scale raw text, creating a significant barrier to foundational research. In contrast, biological systems demonstrate highly sample-efficient learning through multi-timescale processing, such as the functional organization of the frontoparietal loop. Taking this as inspiration, we introduce HRM-Text, which replaces standard Transformers with a Hierarchical Recurrent Model (HRM) that decouples computation into slow-evolving strategic and fast-evolving execution layers. To stabilize this deep recurrence for language modeling, we introduce MagicNorm and warmup deep credit assignment. Furthermore, instead of standard raw-text pretraining, we train exclusively on instruction-response pairs using a task-completion objective and PrefixLM masking. Serving as an empirical existence proof of efficient pretraining, a 1B-parameter HRM-Text model trained from scratch on only 40 billion unique tokens and $1,500 budget achieves 60.7% on MMLU, 81.9% on ARC-C, 82.2% on DROP, 84.5% on GSM8K, and 56.2% on MATH. Despite utilizing roughly 100-900x fewer training tokens and 96-432x less estimated compute than standard baselines, HRM-Text performs competitively with 2-7B parameter open models. These results demonstrate that co-designing architectures and objectives can radically reduce the compute-to-performance ratio, making pretraining from scratch accessible to the broader research community.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2605.20613 [cs.CL] |
| (or arXiv:2605.20613v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.20613 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yuhao Sun [view email]
[v1]
Wed, 20 May 2026 01:59:50 UTC (2,349 KB)
— Originally published at arxiv.org
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