A 1B-parameter hierarchical recurrent model pretrained on 40B instruction-response tokens achieves 60.7% MMLU and strong results on ARC-C, DROP, GSM8K, and MATH while using 100-900x fewer tokens than standard baselines.
Neural gradients are near- lognormal: improved quantized and sparse training
2 Pith papers cite this work. Polarity classification is still indexing.
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Deployment-aligned low-precision NAS recovers about two-thirds of the accuracy drop from post-training quantization, achieving 0.826 mIoU on-device for a 95k-parameter model on Intel Movidius Myriad X without added complexity.
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HRM-Text: Efficient Pretraining Beyond Scaling
A 1B-parameter hierarchical recurrent model pretrained on 40B instruction-response tokens achieves 60.7% MMLU and strong results on ARC-C, DROP, GSM8K, and MATH while using 100-900x fewer tokens than standard baselines.
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Deployment-Aligned Low-Precision Neural Architecture Search for Spaceborne Edge AI
Deployment-aligned low-precision NAS recovers about two-thirds of the accuracy drop from post-training quantization, achieving 0.826 mIoU on-device for a 95k-parameter model on Intel Movidius Myriad X without added complexity.