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Gated Delta Networks scaling rules improve LLM training stability

By PulseAugur Editorial · [2 sources] ·

Researchers have developed new scaling rules for Gated Delta Networks, a type of neural network architecture. These rules, derived through a method called coordinate-size estimation propagation, allow for stable learning rate transfer across different model widths. Experiments on language model pre-training demonstrate that these configurations improve learning stability with optimizers like AdamW and SGD, unlike standard parameterization methods. AI

IMPACT Enables more stable and efficient training of large language models by providing better hyperparameter tuning across different model sizes.

RANK_REASON The cluster contains an academic paper detailing new methods for neural network architectures and training.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Gated Delta Networks scaling rules improve LLM training stability

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The cluster contains an academic paper detailing new methods for neural network architectures and training.
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paper, model release
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116 days old
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Yifeng Liu, Quanquan Gu ·

    Unlocking Feature Learning in Gated Delta Networks at Scale

    arXiv:2606.04048v1 Announce Type: cross Abstract: Training and scaling Large Language Models demand enormous computational resources, motivating both efficient sub-quadratic architectures and principled hyperparameter tuning methods. While the Maximal Update Parametrization ($\mu…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Unlocking Feature Learning in Gated Delta Networks at Scale

    Scaling rules for Gated Delta Networks are derived through coordinate-size estimation propagation, enabling stable learning-rate transfer across model widths with both AdamW and SGD optimizers.

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